SlideShare a Scribd company logo

[IUI 2017] Criteria Chains: A Novel Multi-Criteria Recommendation Approach

•
•
YONG ZHENG
YONG ZHENG

Criteria Chains: A Novel Multi-Criteria Recommendation Approach

Technology
1 of 18
Download to read offline
Criteria Chains: A Novel Multi-Criteria Recommendation Approach Yong Zheng Illinois Institute of Technology Chicago, IL, 60616, USA ACM Conference on Intelligent User Interfaces Limassol, Cyprus, March 13-16, 2017
Recommender System (RS) • RS: item recommendations tailored to user tastes 2
Traditional RS: Ratings By Users on Items 3 Red Mars Juras- sic Park Lost World 2001 Found ation Differ- ence Engine Recommender Systems User Profile Neuro- mancer 2010 Recommendations
4 Multi-Criteria Recommender Systems
5 Multi-Criteria Recommender Systems
6 Multi-Criteria Recommender Systems • Traditional RS: • Multi-Criteria RS: R0 is a user’s overall rating on the item. R1, R2, …, Rk are ratings on item aspects.
7 Multi-Criteria Recommender Systems Research Problems in Multi-Criteria RS Step2 Multi-Criteria RatingsStep 1 Step 1 Step 1. Learn from knowledge to predict multi-criteria ratings Step 2. Aggregate multi-criteria ratings to predict the overall rating. Linear Regression:
8 Multi-Criteria Recommender Systems There are two solutions to improve it: • Improve the predicted multi-criteria ratings • Better utilize them to estimate the overall rating The contributions by Criteria Chains: • Better predict multi-criteria ratings • Figure out a new way to aggregate these ratings
9 Criteria Chains Assumptions in Criteria Chains • Multi-criteria ratings can be viewed as contexts • Ratings can be predicted in a chain
10 Criteria Chains Assumptions in Criteria Chains • Multi-criteria ratings can be viewed as contexts • Ratings can be predicted in a chain First, predict U3’s rating on Room Next, take U3’s rating on room as contexts, User + Item + Room  Check-in Again, take previous predictions as contexts, User + Item + Room + Check-in  Service The prediction process works like a chain: Room  Check-in  Service
11 Criteria Chains The sequence of the chain matters • Random Sequence • Rank by Lower Prediction Errors • Rank by Information Gain
12 Criteria Chains How to predict the final overall rating? • Criteria Chain: Aggregation Model (CCA) Linear regression by predicted multi-criteria ratings • Criteria Chain: Contextual Model (CCC) Direct prediction by viewing the predicted multi- criteria ratings as context information • Criteria-Independent Contextual Model (CIC) This is a baseline approach. We predicted multi- criteria ratings independently and use them as context to predict the final overall rating
13 Experimental Setting • Data Sets • Evaluations – Five-fold Cross Validation – Rating Prediction: Mean Absolute Error (MAE) – Top-N Recommendation: Precision, Recall, NDCG – We use CAMF_C for context-aware recommendations User Item Rating # of Criteria TripAdvisor 1,502 14,300 22,130 7 Yahoo!Movie 2,162 3,075 49,351 4
14 Experimental Results The overall result demonstrates CCA and CCC outperform baselines, CCC is the best
15 Experimental Results Criteria Chains is able to improve the predictions on individual ratings on criterion
16 Experimental Results Information Gain is the best way to produce the optimal sequence
17 Conclusions and Future Work • Criteria Chains work better than baseline approaches • Criteria Chains take correlations among multiple criteria into consideration • Information Gain is the best way to produce chain • Using multi-criteria ratings as contexts, CCC, is the best approach after predictions on multiple ratings • Future Work: figure out optimal ways to generate the chain sequence in addition to information gain.
Criteria Chains: A Novel Multi-Criteria Recommendation Approach Yong Zheng Illinois Institute of Technology Chicago, IL, 60616, USA ACM Conference on Intelligent User Interfaces Limassol, Cyprus, March 13-16, 2017

Recommended

[UMAP 2016] User-Oriented Context Suggestion
[UMAP 2016] User-Oriented Context Suggestion[UMAP 2016] User-Oriented Context Suggestion
[UMAP 2016] User-Oriented Context SuggestionYONG ZHENG
 
Context-aware Recommendation: A Quick View
Context-aware Recommendation: A Quick ViewContext-aware Recommendation: A Quick View
Context-aware Recommendation: A Quick ViewYONG ZHENG
 
[SAC 2015] Improve General Contextual SLIM Recommendation Algorithms By Facto...
[SAC 2015] Improve General Contextual SLIM Recommendation Algorithms By Facto...[SAC 2015] Improve General Contextual SLIM Recommendation Algorithms By Facto...
[SAC 2015] Improve General Contextual SLIM Recommendation Algorithms By Facto...YONG ZHENG
 
[UMAP 2015] Integrating Context Similarity with Sparse Linear Recommendation ...
[UMAP 2015] Integrating Context Similarity with Sparse Linear Recommendation ...[UMAP 2015] Integrating Context Similarity with Sparse Linear Recommendation ...
[UMAP 2015] Integrating Context Similarity with Sparse Linear Recommendation ...YONG ZHENG
 
[WI 2017] Affective Prediction By Collaborative Chains In Movie Recommendation
[WI 2017] Affective Prediction By Collaborative Chains In Movie Recommendation[WI 2017] Affective Prediction By Collaborative Chains In Movie Recommendation
[WI 2017] Affective Prediction By Collaborative Chains In Movie RecommendationYONG ZHENG
 
[EMPIRE 2016] Adapt to Emotional Reactions In Context-aware Personalization
[EMPIRE 2016] Adapt to Emotional Reactions In Context-aware Personalization[EMPIRE 2016] Adapt to Emotional Reactions In Context-aware Personalization
[EMPIRE 2016] Adapt to Emotional Reactions In Context-aware PersonalizationYONG ZHENG
 
[WI 2017] Context Suggestion: Empirical Evaluations vs User Studies
[WI 2017] Context Suggestion: Empirical Evaluations vs User Studies[WI 2017] Context Suggestion: Empirical Evaluations vs User Studies
[WI 2017] Context Suggestion: Empirical Evaluations vs User StudiesYONG ZHENG
 
[ADMA 2017] Identification of Grey Sheep Users By Histogram Intersection In R...
[ADMA 2017] Identification of Grey Sheep Users By Histogram Intersection In R...[ADMA 2017] Identification of Grey Sheep Users By Histogram Intersection In R...
[ADMA 2017] Identification of Grey Sheep Users By Histogram Intersection In R...YONG ZHENG
 

Recommended

[UMAP 2016] User-Oriented Context Suggestion
[UMAP 2016] User-Oriented Context Suggestion[UMAP 2016] User-Oriented Context Suggestion
[UMAP 2016] User-Oriented Context SuggestionYONG ZHENG
 
Context-aware Recommendation: A Quick View
Context-aware Recommendation: A Quick ViewContext-aware Recommendation: A Quick View
Context-aware Recommendation: A Quick ViewYONG ZHENG
 
[SAC 2015] Improve General Contextual SLIM Recommendation Algorithms By Facto...
[SAC 2015] Improve General Contextual SLIM Recommendation Algorithms By Facto...[SAC 2015] Improve General Contextual SLIM Recommendation Algorithms By Facto...
[SAC 2015] Improve General Contextual SLIM Recommendation Algorithms By Facto...YONG ZHENG
 
[UMAP 2015] Integrating Context Similarity with Sparse Linear Recommendation ...
[UMAP 2015] Integrating Context Similarity with Sparse Linear Recommendation ...[UMAP 2015] Integrating Context Similarity with Sparse Linear Recommendation ...
[UMAP 2015] Integrating Context Similarity with Sparse Linear Recommendation ...YONG ZHENG
 
[WI 2017] Affective Prediction By Collaborative Chains In Movie Recommendation
[WI 2017] Affective Prediction By Collaborative Chains In Movie Recommendation[WI 2017] Affective Prediction By Collaborative Chains In Movie Recommendation
[WI 2017] Affective Prediction By Collaborative Chains In Movie RecommendationYONG ZHENG
 
[EMPIRE 2016] Adapt to Emotional Reactions In Context-aware Personalization
[EMPIRE 2016] Adapt to Emotional Reactions In Context-aware Personalization[EMPIRE 2016] Adapt to Emotional Reactions In Context-aware Personalization
[EMPIRE 2016] Adapt to Emotional Reactions In Context-aware PersonalizationYONG ZHENG
 
[WI 2017] Context Suggestion: Empirical Evaluations vs User Studies
[WI 2017] Context Suggestion: Empirical Evaluations vs User Studies[WI 2017] Context Suggestion: Empirical Evaluations vs User Studies
[WI 2017] Context Suggestion: Empirical Evaluations vs User StudiesYONG ZHENG
 
[ADMA 2017] Identification of Grey Sheep Users By Histogram Intersection In R...
[ADMA 2017] Identification of Grey Sheep Users By Histogram Intersection In R...[ADMA 2017] Identification of Grey Sheep Users By Histogram Intersection In R...
[ADMA 2017] Identification of Grey Sheep Users By Histogram Intersection In R...YONG ZHENG
 
[Decisions2013@RecSys]The Role of Emotions in Context-aware Recommendation
[Decisions2013@RecSys]The Role of Emotions in Context-aware Recommendation[Decisions2013@RecSys]The Role of Emotions in Context-aware Recommendation
[Decisions2013@RecSys]The Role of Emotions in Context-aware RecommendationYONG ZHENG
 
[RIIT 2017] Identifying Grey Sheep Users By The Distribution of User Similari...
[RIIT 2017] Identifying Grey Sheep Users By The Distribution of User Similari...[RIIT 2017] Identifying Grey Sheep Users By The Distribution of User Similari...
[RIIT 2017] Identifying Grey Sheep Users By The Distribution of User Similari...YONG ZHENG
 
Tutorial: Context In Recommender Systems
Tutorial: Context In Recommender SystemsTutorial: Context In Recommender Systems
Tutorial: Context In Recommender SystemsYONG ZHENG
 
[IUI2015] A Revisit to The Identification of Contexts in Recommender Systems
[IUI2015] A Revisit to The Identification of Contexts in Recommender Systems[IUI2015] A Revisit to The Identification of Contexts in Recommender Systems
[IUI2015] A Revisit to The Identification of Contexts in Recommender SystemsYONG ZHENG
 
Hybridisation Techniques for Cold-Starting Context-Aware Recommender Systems
Hybridisation Techniques for Cold-Starting Context-Aware Recommender SystemsHybridisation Techniques for Cold-Starting Context-Aware Recommender Systems
Hybridisation Techniques for Cold-Starting Context-Aware Recommender SystemsMatthias Braunhofer
 
Movie lens movie recommendation system
Movie lens movie recommendation systemMovie lens movie recommendation system
Movie lens movie recommendation systemGaurav Sawant
 
Summary of a Recommender Systems Survey paper
Summary of a Recommender Systems Survey paperSummary of a Recommender Systems Survey paper
Summary of a Recommender Systems Survey paperChangsung Moon
 
Hybrid Solution of the Cold-Start Problem in Context-Aware Recommender Systems
Hybrid Solution of the Cold-Start Problem in Context-Aware Recommender SystemsHybrid Solution of the Cold-Start Problem in Context-Aware Recommender Systems
Hybrid Solution of the Cold-Start Problem in Context-Aware Recommender SystemsMatthias Braunhofer
 
Recommender Systems
Recommender SystemsRecommender Systems
Recommender SystemsLior Rokach
 
Delayed Rewards in the context of Reinforcement Learning based Recommender ...
Delayed Rewards in the context of Reinforcement Learning based Recommender ...Delayed Rewards in the context of Reinforcement Learning based Recommender ...
Delayed Rewards in the context of Reinforcement Learning based Recommender ...Debmalya Biswas
 
GTC 2021: Counterfactual Learning to Rank in E-commerce
GTC 2021: Counterfactual Learning to Rank in E-commerceGTC 2021: Counterfactual Learning to Rank in E-commerce
GTC 2021: Counterfactual Learning to Rank in E-commerceGrubhubTech
 
Movie recommendation project
Movie recommendation projectMovie recommendation project
Movie recommendation projectAbhishek Jaisingh
 
Recommender Systems
Recommender SystemsRecommender Systems
Recommender SystemsFederico Cargnelutti
 
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Kishor Datta Gupta
 
Item Based Collaborative Filtering Recommendation Algorithms
Item Based Collaborative Filtering Recommendation AlgorithmsItem Based Collaborative Filtering Recommendation Algorithms
Item Based Collaborative Filtering Recommendation Algorithmsnextlib
 
Alleviating cold-user start problem with users' social network data in recomm...
Alleviating cold-user start problem with users' social network data in recomm...Alleviating cold-user start problem with users' social network data in recomm...
Alleviating cold-user start problem with users' social network data in recomm...Eduardo Castillejo Gil
 
Movie recommendation system using collaborative filtering system
Movie recommendation system using collaborative filtering system Movie recommendation system using collaborative filtering system
Movie recommendation system using collaborative filtering system Mauryasuraj98
 
Recommender Systems! @ASAI 2011
Recommender Systems! @ASAI 2011Recommender Systems! @ASAI 2011
Recommender Systems! @ASAI 2011Ernesto Mislej
 
Collaborative Filtering using KNN
Collaborative Filtering using KNNCollaborative Filtering using KNN
Collaborative Filtering using KNNŞeyda Hatipoğlu
 
Movies recommendation system in R Studio, Machine learning
Movies recommendation system in R Studio, Machine learning Movies recommendation system in R Studio, Machine learning
Movies recommendation system in R Studio, Machine learning Mauryasuraj98
 
[CIKM 2014] Deviation-Based Contextual SLIM Recommenders
[CIKM 2014] Deviation-Based Contextual SLIM Recommenders[CIKM 2014] Deviation-Based Contextual SLIM Recommenders
[CIKM 2014] Deviation-Based Contextual SLIM RecommendersYONG ZHENG
 
[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...
[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...
[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...YONG ZHENG
 

More Related Content

What's hot

[Decisions2013@RecSys]The Role of Emotions in Context-aware Recommendation
[Decisions2013@RecSys]The Role of Emotions in Context-aware Recommendation[Decisions2013@RecSys]The Role of Emotions in Context-aware Recommendation
[Decisions2013@RecSys]The Role of Emotions in Context-aware RecommendationYONG ZHENG
 
[RIIT 2017] Identifying Grey Sheep Users By The Distribution of User Similari...
[RIIT 2017] Identifying Grey Sheep Users By The Distribution of User Similari...[RIIT 2017] Identifying Grey Sheep Users By The Distribution of User Similari...
[RIIT 2017] Identifying Grey Sheep Users By The Distribution of User Similari...YONG ZHENG
 
Tutorial: Context In Recommender Systems
Tutorial: Context In Recommender SystemsTutorial: Context In Recommender Systems
Tutorial: Context In Recommender SystemsYONG ZHENG
 
[IUI2015] A Revisit to The Identification of Contexts in Recommender Systems
[IUI2015] A Revisit to The Identification of Contexts in Recommender Systems[IUI2015] A Revisit to The Identification of Contexts in Recommender Systems
[IUI2015] A Revisit to The Identification of Contexts in Recommender SystemsYONG ZHENG
 
Hybridisation Techniques for Cold-Starting Context-Aware Recommender Systems
Hybridisation Techniques for Cold-Starting Context-Aware Recommender SystemsHybridisation Techniques for Cold-Starting Context-Aware Recommender Systems
Hybridisation Techniques for Cold-Starting Context-Aware Recommender SystemsMatthias Braunhofer
 
Movie lens movie recommendation system
Movie lens movie recommendation systemMovie lens movie recommendation system
Movie lens movie recommendation systemGaurav Sawant
 
Summary of a Recommender Systems Survey paper
Summary of a Recommender Systems Survey paperSummary of a Recommender Systems Survey paper
Summary of a Recommender Systems Survey paperChangsung Moon
 
Hybrid Solution of the Cold-Start Problem in Context-Aware Recommender Systems
Hybrid Solution of the Cold-Start Problem in Context-Aware Recommender SystemsHybrid Solution of the Cold-Start Problem in Context-Aware Recommender Systems
Hybrid Solution of the Cold-Start Problem in Context-Aware Recommender SystemsMatthias Braunhofer
 
Recommender Systems
Recommender SystemsRecommender Systems
Recommender SystemsLior Rokach
 
Delayed Rewards in the context of Reinforcement Learning based Recommender ...
Delayed Rewards in the context of Reinforcement Learning based Recommender ...Delayed Rewards in the context of Reinforcement Learning based Recommender ...
Delayed Rewards in the context of Reinforcement Learning based Recommender ...Debmalya Biswas
 
GTC 2021: Counterfactual Learning to Rank in E-commerce
GTC 2021: Counterfactual Learning to Rank in E-commerceGTC 2021: Counterfactual Learning to Rank in E-commerce
GTC 2021: Counterfactual Learning to Rank in E-commerceGrubhubTech
 
Movie recommendation project
Movie recommendation projectMovie recommendation project
Movie recommendation projectAbhishek Jaisingh
 
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Kishor Datta Gupta
 
Item Based Collaborative Filtering Recommendation Algorithms
Item Based Collaborative Filtering Recommendation AlgorithmsItem Based Collaborative Filtering Recommendation Algorithms
Item Based Collaborative Filtering Recommendation Algorithmsnextlib
 
Alleviating cold-user start problem with users' social network data in recomm...
Alleviating cold-user start problem with users' social network data in recomm...Alleviating cold-user start problem with users' social network data in recomm...
Alleviating cold-user start problem with users' social network data in recomm...Eduardo Castillejo Gil
 
Movie recommendation system using collaborative filtering system
Movie recommendation system using collaborative filtering system Movie recommendation system using collaborative filtering system
Movie recommendation system using collaborative filtering system Mauryasuraj98
 
Recommender Systems! @ASAI 2011
Recommender Systems! @ASAI 2011Recommender Systems! @ASAI 2011
Recommender Systems! @ASAI 2011Ernesto Mislej
 
Collaborative Filtering using KNN
Collaborative Filtering using KNNCollaborative Filtering using KNN
Collaborative Filtering using KNNŞeyda Hatipoğlu
 
Movies recommendation system in R Studio, Machine learning
Movies recommendation system in R Studio, Machine learning Movies recommendation system in R Studio, Machine learning
Movies recommendation system in R Studio, Machine learning Mauryasuraj98
 

What's hot (20)

[Decisions2013@RecSys]The Role of Emotions in Context-aware Recommendation
[Decisions2013@RecSys]The Role of Emotions in Context-aware Recommendation[Decisions2013@RecSys]The Role of Emotions in Context-aware Recommendation
[Decisions2013@RecSys]The Role of Emotions in Context-aware Recommendation
 
[RIIT 2017] Identifying Grey Sheep Users By The Distribution of User Similari...
[RIIT 2017] Identifying Grey Sheep Users By The Distribution of User Similari...[RIIT 2017] Identifying Grey Sheep Users By The Distribution of User Similari...
[RIIT 2017] Identifying Grey Sheep Users By The Distribution of User Similari...
 
Tutorial: Context In Recommender Systems
Tutorial: Context In Recommender SystemsTutorial: Context In Recommender Systems
Tutorial: Context In Recommender Systems
 
[IUI2015] A Revisit to The Identification of Contexts in Recommender Systems
[IUI2015] A Revisit to The Identification of Contexts in Recommender Systems[IUI2015] A Revisit to The Identification of Contexts in Recommender Systems
[IUI2015] A Revisit to The Identification of Contexts in Recommender Systems
 
Hybridisation Techniques for Cold-Starting Context-Aware Recommender Systems
Hybridisation Techniques for Cold-Starting Context-Aware Recommender SystemsHybridisation Techniques for Cold-Starting Context-Aware Recommender Systems
Hybridisation Techniques for Cold-Starting Context-Aware Recommender Systems
 
Movie lens movie recommendation system
Movie lens movie recommendation systemMovie lens movie recommendation system
Movie lens movie recommendation system
 
Summary of a Recommender Systems Survey paper
Summary of a Recommender Systems Survey paperSummary of a Recommender Systems Survey paper
Summary of a Recommender Systems Survey paper
 
Hybrid Solution of the Cold-Start Problem in Context-Aware Recommender Systems
Hybrid Solution of the Cold-Start Problem in Context-Aware Recommender SystemsHybrid Solution of the Cold-Start Problem in Context-Aware Recommender Systems
Hybrid Solution of the Cold-Start Problem in Context-Aware Recommender Systems
 
Recommender Systems
Recommender SystemsRecommender Systems
Recommender Systems
 
Delayed Rewards in the context of Reinforcement Learning based Recommender ...
Delayed Rewards in the context of Reinforcement Learning based Recommender ...Delayed Rewards in the context of Reinforcement Learning based Recommender ...
Delayed Rewards in the context of Reinforcement Learning based Recommender ...
 
GTC 2021: Counterfactual Learning to Rank in E-commerce
GTC 2021: Counterfactual Learning to Rank in E-commerceGTC 2021: Counterfactual Learning to Rank in E-commerce
GTC 2021: Counterfactual Learning to Rank in E-commerce
 
Movie recommendation project
Movie recommendation projectMovie recommendation project
Movie recommendation project
 
Recommender Systems
Recommender SystemsRecommender Systems
Recommender Systems
 
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
 
Item Based Collaborative Filtering Recommendation Algorithms
Item Based Collaborative Filtering Recommendation AlgorithmsItem Based Collaborative Filtering Recommendation Algorithms
Item Based Collaborative Filtering Recommendation Algorithms
 
Alleviating cold-user start problem with users' social network data in recomm...
Alleviating cold-user start problem with users' social network data in recomm...Alleviating cold-user start problem with users' social network data in recomm...
Alleviating cold-user start problem with users' social network data in recomm...
 
Movie recommendation system using collaborative filtering system
Movie recommendation system using collaborative filtering system Movie recommendation system using collaborative filtering system
Movie recommendation system using collaborative filtering system
 
Recommender Systems! @ASAI 2011
Recommender Systems! @ASAI 2011Recommender Systems! @ASAI 2011
Recommender Systems! @ASAI 2011
 
Collaborative Filtering using KNN
Collaborative Filtering using KNNCollaborative Filtering using KNN
Collaborative Filtering using KNN
 
Movies recommendation system in R Studio, Machine learning
Movies recommendation system in R Studio, Machine learning Movies recommendation system in R Studio, Machine learning
Movies recommendation system in R Studio, Machine learning
 

Viewers also liked

[CIKM 2014] Deviation-Based Contextual SLIM Recommenders
[CIKM 2014] Deviation-Based Contextual SLIM Recommenders[CIKM 2014] Deviation-Based Contextual SLIM Recommenders
[CIKM 2014] Deviation-Based Contextual SLIM RecommendersYONG ZHENG
 
[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...
[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...
[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...YONG ZHENG
 
[SAC2014]Splitting Approaches for Context-Aware Recommendation: An Empirical ...
[SAC2014]Splitting Approaches for Context-Aware Recommendation: An Empirical ...[SAC2014]Splitting Approaches for Context-Aware Recommendation: An Empirical ...
[SAC2014]Splitting Approaches for Context-Aware Recommendation: An Empirical ...YONG ZHENG
 
[WISE 2015] Similarity-Based Context-aware Recommendation
[WISE 2015] Similarity-Based Context-aware Recommendation[WISE 2015] Similarity-Based Context-aware Recommendation
[WISE 2015] Similarity-Based Context-aware RecommendationYONG ZHENG
 
Tutorial: Context-awareness In Information Retrieval and Recommender Systems
Tutorial: Context-awareness In Information Retrieval and Recommender SystemsTutorial: Context-awareness In Information Retrieval and Recommender Systems
Tutorial: Context-awareness In Information Retrieval and Recommender SystemsYONG ZHENG
 
Matrix Factorization In Recommender Systems
Matrix Factorization In Recommender SystemsMatrix Factorization In Recommender Systems
Matrix Factorization In Recommender SystemsYONG ZHENG
 

Viewers also liked (6)

[CIKM 2014] Deviation-Based Contextual SLIM Recommenders
[CIKM 2014] Deviation-Based Contextual SLIM Recommenders[CIKM 2014] Deviation-Based Contextual SLIM Recommenders
[CIKM 2014] Deviation-Based Contextual SLIM Recommenders
 
[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...
[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...
[RecSys 2014] Deviation-Based and Similarity-Based Contextual SLIM Recommenda...
 
[SAC2014]Splitting Approaches for Context-Aware Recommendation: An Empirical ...
[SAC2014]Splitting Approaches for Context-Aware Recommendation: An Empirical ...[SAC2014]Splitting Approaches for Context-Aware Recommendation: An Empirical ...
[SAC2014]Splitting Approaches for Context-Aware Recommendation: An Empirical ...
 
[WISE 2015] Similarity-Based Context-aware Recommendation
[WISE 2015] Similarity-Based Context-aware Recommendation[WISE 2015] Similarity-Based Context-aware Recommendation
[WISE 2015] Similarity-Based Context-aware Recommendation
 
Tutorial: Context-awareness In Information Retrieval and Recommender Systems
Tutorial: Context-awareness In Information Retrieval and Recommender SystemsTutorial: Context-awareness In Information Retrieval and Recommender Systems
Tutorial: Context-awareness In Information Retrieval and Recommender Systems
 
Matrix Factorization In Recommender Systems
Matrix Factorization In Recommender SystemsMatrix Factorization In Recommender Systems
Matrix Factorization In Recommender Systems
 

Similar to [IUI 2017] Criteria Chains: A Novel Multi-Criteria Recommendation Approach

Rokach-GomaxSlides.pptx
Rokach-GomaxSlides.pptxRokach-GomaxSlides.pptx
Rokach-GomaxSlides.pptxJadna Almeida
 
Rokach-GomaxSlides (1).pptx
Rokach-GomaxSlides (1).pptxRokach-GomaxSlides (1).pptx
Rokach-GomaxSlides (1).pptxJadna Almeida
 
ACM ICTIR 2019 Slides - Santa Clara, USA
ACM ICTIR 2019 Slides - Santa Clara, USAACM ICTIR 2019 Slides - Santa Clara, USA
ACM ICTIR 2019 Slides - Santa Clara, USAIadh Ounis
 
Best Practices in Recommender System Challenges
Best Practices in Recommender System ChallengesBest Practices in Recommender System Challenges
Best Practices in Recommender System ChallengesAlan Said
 
Cikm 2013 - Beyond Data From User Information to Business Value
Cikm 2013 - Beyond Data From User Information to Business ValueCikm 2013 - Beyond Data From User Information to Business Value
Cikm 2013 - Beyond Data From User Information to Business ValueXavier Amatriain
 
Preference Elicitation Interface
Preference Elicitation InterfacePreference Elicitation Interface
Preference Elicitation Interface晓愚 孟
 
Empirical Evaluation of Active Learning in Recommender Systems
Empirical Evaluation of Active Learning in Recommender SystemsEmpirical Evaluation of Active Learning in Recommender Systems
Empirical Evaluation of Active Learning in Recommender SystemsUniversity of Bergen
 
Analysis, design and implementation of a Multi-Criteria Recommender System ba...
Analysis, design and implementation of a Multi-Criteria Recommender System ba...Analysis, design and implementation of a Multi-Criteria Recommender System ba...
Analysis, design and implementation of a Multi-Criteria Recommender System ba...Davide Giannico
 
[UPDATE] Udacity webinar on Recommendation Systems
[UPDATE] Udacity webinar on Recommendation Systems[UPDATE] Udacity webinar on Recommendation Systems
[UPDATE] Udacity webinar on Recommendation SystemsAxel de Romblay
 
1440 track 2 boire_using our laptop
1440 track 2 boire_using our laptop1440 track 2 boire_using our laptop
1440 track 2 boire_using our laptopRising Media, Inc.
 
Pattern recognition UNIT 5
Pattern recognition UNIT 5Pattern recognition UNIT 5
Pattern recognition UNIT 5SURBHI SAROHA
 
AI_Paper_Presentation
AI_Paper_PresentationAI_Paper_Presentation
AI_Paper_PresentationNaveed Afzal
 
Udacity webinar on Recommendation Systems
Udacity webinar on Recommendation SystemsUdacity webinar on Recommendation Systems
Udacity webinar on Recommendation SystemsAxel de Romblay
 
RS in the context of Big Data-v4
RS in the context of Big Data-v4RS in the context of Big Data-v4
RS in the context of Big Data-v4Khadija Atiya
 
Guiding through a typical Machine Learning Pipeline
Guiding through a typical Machine Learning PipelineGuiding through a typical Machine Learning Pipeline
Guiding through a typical Machine Learning PipelineMichael Gerke
 
IRJET- Online Course Recommendation System
IRJET- Online Course Recommendation SystemIRJET- Online Course Recommendation System
IRJET- Online Course Recommendation SystemIRJET Journal
 
Movie recommendation Engine using Artificial Intelligence
Movie recommendation Engine using Artificial IntelligenceMovie recommendation Engine using Artificial Intelligence
Movie recommendation Engine using Artificial IntelligenceHarivamshi D
 

Similar to [IUI 2017] Criteria Chains: A Novel Multi-Criteria Recommendation Approach (20)

Rokach-GomaxSlides.pptx
Rokach-GomaxSlides.pptxRokach-GomaxSlides.pptx
Rokach-GomaxSlides.pptx
 
Rokach-GomaxSlides (1).pptx
Rokach-GomaxSlides (1).pptxRokach-GomaxSlides (1).pptx
Rokach-GomaxSlides (1).pptx
 
ACM ICTIR 2019 Slides - Santa Clara, USA
ACM ICTIR 2019 Slides - Santa Clara, USAACM ICTIR 2019 Slides - Santa Clara, USA
ACM ICTIR 2019 Slides - Santa Clara, USA
 
Best Practices in Recommender System Challenges
Best Practices in Recommender System ChallengesBest Practices in Recommender System Challenges
Best Practices in Recommender System Challenges
 
Cikm 2013 - Beyond Data From User Information to Business Value
Cikm 2013 - Beyond Data From User Information to Business ValueCikm 2013 - Beyond Data From User Information to Business Value
Cikm 2013 - Beyond Data From User Information to Business Value
 
Preference Elicitation Interface
Preference Elicitation InterfacePreference Elicitation Interface
Preference Elicitation Interface
 
Empirical Evaluation of Active Learning in Recommender Systems
Empirical Evaluation of Active Learning in Recommender SystemsEmpirical Evaluation of Active Learning in Recommender Systems
Empirical Evaluation of Active Learning in Recommender Systems
 
Analysis, design and implementation of a Multi-Criteria Recommender System ba...
Analysis, design and implementation of a Multi-Criteria Recommender System ba...Analysis, design and implementation of a Multi-Criteria Recommender System ba...
Analysis, design and implementation of a Multi-Criteria Recommender System ba...
 
slides-sd
slides-sdslides-sd
slides-sd
 
[UPDATE] Udacity webinar on Recommendation Systems
[UPDATE] Udacity webinar on Recommendation Systems[UPDATE] Udacity webinar on Recommendation Systems
[UPDATE] Udacity webinar on Recommendation Systems
 
ASME14_Ningrong
ASME14_NingrongASME14_Ningrong
ASME14_Ningrong
 
1440 track 2 boire_using our laptop
1440 track 2 boire_using our laptop1440 track 2 boire_using our laptop
1440 track 2 boire_using our laptop
 
Pattern recognition UNIT 5
Pattern recognition UNIT 5Pattern recognition UNIT 5
Pattern recognition UNIT 5
 
AI_Paper_Presentation
AI_Paper_PresentationAI_Paper_Presentation
AI_Paper_Presentation
 
Udacity webinar on Recommendation Systems
Udacity webinar on Recommendation SystemsUdacity webinar on Recommendation Systems
Udacity webinar on Recommendation Systems
 
RS in the context of Big Data-v4
RS in the context of Big Data-v4RS in the context of Big Data-v4
RS in the context of Big Data-v4
 
Guiding through a typical Machine Learning Pipeline
Guiding through a typical Machine Learning PipelineGuiding through a typical Machine Learning Pipeline
Guiding through a typical Machine Learning Pipeline
 
Ds for finance day 3
Ds for finance day 3Ds for finance day 3
Ds for finance day 3
 
IRJET- Online Course Recommendation System
IRJET- Online Course Recommendation SystemIRJET- Online Course Recommendation System
IRJET- Online Course Recommendation System
 
Movie recommendation Engine using Artificial Intelligence
Movie recommendation Engine using Artificial IntelligenceMovie recommendation Engine using Artificial Intelligence
Movie recommendation Engine using Artificial Intelligence
 

More from YONG ZHENG

[WI 2014]Context Recommendation Using Multi-label Classification
[WI 2014]Context Recommendation Using Multi-label Classification[WI 2014]Context Recommendation Using Multi-label Classification
[WI 2014]Context Recommendation Using Multi-label ClassificationYONG ZHENG
 
[UMAP2013]Tutorial on Context-Aware User Modeling for Recommendation by Bamsh...
[UMAP2013]Tutorial on Context-Aware User Modeling for Recommendation by Bamsh...[UMAP2013]Tutorial on Context-Aware User Modeling for Recommendation by Bamsh...
[UMAP2013]Tutorial on Context-Aware User Modeling for Recommendation by Bamsh...YONG ZHENG
 
[UMAP2013] Recommendation with Differential Context Weighting
[UMAP2013] Recommendation with Differential Context Weighting[UMAP2013] Recommendation with Differential Context Weighting
[UMAP2013] Recommendation with Differential Context WeightingYONG ZHENG
 
[SOCRS2013]Differential Context Modeling in Collaborative Filtering
[SOCRS2013]Differential Context Modeling in Collaborative Filtering[SOCRS2013]Differential Context Modeling in Collaborative Filtering
[SOCRS2013]Differential Context Modeling in Collaborative FilteringYONG ZHENG
 
Slope one recommender on hadoop
Slope one recommender on hadoopSlope one recommender on hadoop
Slope one recommender on hadoopYONG ZHENG
 
A manual for Ph.D dissertation
A manual for Ph.D dissertationA manual for Ph.D dissertation
A manual for Ph.D dissertationYONG ZHENG
 
Attention flow by tagging prediction
Attention flow by tagging predictionAttention flow by tagging prediction
Attention flow by tagging predictionYONG ZHENG
 
[CARS2012@RecSys]Optimal Feature Selection for Context-Aware Recommendation u...
[CARS2012@RecSys]Optimal Feature Selection for Context-Aware Recommendation u...[CARS2012@RecSys]Optimal Feature Selection for Context-Aware Recommendation u...
[CARS2012@RecSys]Optimal Feature Selection for Context-Aware Recommendation u...YONG ZHENG
 
[ECWEB2012]Differential Context Relaxation for Context-Aware Travel Recommend...
[ECWEB2012]Differential Context Relaxation for Context-Aware Travel Recommend...[ECWEB2012]Differential Context Relaxation for Context-Aware Travel Recommend...
[ECWEB2012]Differential Context Relaxation for Context-Aware Travel Recommend...YONG ZHENG
 
[HetRec2011@RecSys]Experience Discovery: Hybrid Recommendation of Student Act...
[HetRec2011@RecSys]Experience Discovery: Hybrid Recommendation of Student Act...[HetRec2011@RecSys]Experience Discovery: Hybrid Recommendation of Student Act...
[HetRec2011@RecSys]Experience Discovery: Hybrid Recommendation of Student Act...YONG ZHENG
 

More from YONG ZHENG (10)

[WI 2014]Context Recommendation Using Multi-label Classification
[WI 2014]Context Recommendation Using Multi-label Classification[WI 2014]Context Recommendation Using Multi-label Classification
[WI 2014]Context Recommendation Using Multi-label Classification
 
[UMAP2013]Tutorial on Context-Aware User Modeling for Recommendation by Bamsh...
[UMAP2013]Tutorial on Context-Aware User Modeling for Recommendation by Bamsh...[UMAP2013]Tutorial on Context-Aware User Modeling for Recommendation by Bamsh...
[UMAP2013]Tutorial on Context-Aware User Modeling for Recommendation by Bamsh...
 
[UMAP2013] Recommendation with Differential Context Weighting
[UMAP2013] Recommendation with Differential Context Weighting[UMAP2013] Recommendation with Differential Context Weighting
[UMAP2013] Recommendation with Differential Context Weighting
 
[SOCRS2013]Differential Context Modeling in Collaborative Filtering
[SOCRS2013]Differential Context Modeling in Collaborative Filtering[SOCRS2013]Differential Context Modeling in Collaborative Filtering
[SOCRS2013]Differential Context Modeling in Collaborative Filtering
 
Slope one recommender on hadoop
Slope one recommender on hadoopSlope one recommender on hadoop
Slope one recommender on hadoop
 
A manual for Ph.D dissertation
A manual for Ph.D dissertationA manual for Ph.D dissertation
A manual for Ph.D dissertation
 
Attention flow by tagging prediction
Attention flow by tagging predictionAttention flow by tagging prediction
Attention flow by tagging prediction
 
[CARS2012@RecSys]Optimal Feature Selection for Context-Aware Recommendation u...
[CARS2012@RecSys]Optimal Feature Selection for Context-Aware Recommendation u...[CARS2012@RecSys]Optimal Feature Selection for Context-Aware Recommendation u...
[CARS2012@RecSys]Optimal Feature Selection for Context-Aware Recommendation u...
 
[ECWEB2012]Differential Context Relaxation for Context-Aware Travel Recommend...
[ECWEB2012]Differential Context Relaxation for Context-Aware Travel Recommend...[ECWEB2012]Differential Context Relaxation for Context-Aware Travel Recommend...
[ECWEB2012]Differential Context Relaxation for Context-Aware Travel Recommend...
 
[HetRec2011@RecSys]Experience Discovery: Hybrid Recommendation of Student Act...
[HetRec2011@RecSys]Experience Discovery: Hybrid Recommendation of Student Act...[HetRec2011@RecSys]Experience Discovery: Hybrid Recommendation of Student Act...
[HetRec2011@RecSys]Experience Discovery: Hybrid Recommendation of Student Act...
 

Recently uploaded

Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfPrecisely
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024Lorenzo Miniero
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxLoriGlavin3
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfMounikaPolabathina
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfAddepto
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxLoriGlavin3
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationSlibray Presentation
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 

Recently uploaded (20)

Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdf
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdf
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special EditionDMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
 

[IUI 2017] Criteria Chains: A Novel Multi-Criteria Recommendation Approach

  • 1. Criteria Chains: A Novel Multi-Criteria Recommendation Approach Yong Zheng Illinois Institute of Technology Chicago, IL, 60616, USA ACM Conference on Intelligent User Interfaces Limassol, Cyprus, March 13-16, 2017
  • 2. Recommender System (RS) • RS: item recommendations tailored to user tastes 2
  • 3. Traditional RS: Ratings By Users on Items 3 Red Mars Juras- sic Park Lost World 2001 Found ation Differ- ence Engine Recommender Systems User Profile Neuro- mancer 2010 Recommendations
  • 6. 6 Multi-Criteria Recommender Systems • Traditional RS: • Multi-Criteria RS: R0 is a user’s overall rating on the item. R1, R2, …, Rk are ratings on item aspects.
  • 7. 7 Multi-Criteria Recommender Systems Research Problems in Multi-Criteria RS Step2 Multi-Criteria RatingsStep 1 Step 1 Step 1. Learn from knowledge to predict multi-criteria ratings Step 2. Aggregate multi-criteria ratings to predict the overall rating. Linear Regression:
  • 8. 8 Multi-Criteria Recommender Systems There are two solutions to improve it: • Improve the predicted multi-criteria ratings • Better utilize them to estimate the overall rating The contributions by Criteria Chains: • Better predict multi-criteria ratings • Figure out a new way to aggregate these ratings
  • 9. 9 Criteria Chains Assumptions in Criteria Chains • Multi-criteria ratings can be viewed as contexts • Ratings can be predicted in a chain
  • 10. 10 Criteria Chains Assumptions in Criteria Chains • Multi-criteria ratings can be viewed as contexts • Ratings can be predicted in a chain First, predict U3’s rating on Room Next, take U3’s rating on room as contexts, User + Item + Room  Check-in Again, take previous predictions as contexts, User + Item + Room + Check-in  Service The prediction process works like a chain: Room  Check-in  Service
  • 11. 11 Criteria Chains The sequence of the chain matters • Random Sequence • Rank by Lower Prediction Errors • Rank by Information Gain
  • 12. 12 Criteria Chains How to predict the final overall rating? • Criteria Chain: Aggregation Model (CCA) Linear regression by predicted multi-criteria ratings • Criteria Chain: Contextual Model (CCC) Direct prediction by viewing the predicted multi- criteria ratings as context information • Criteria-Independent Contextual Model (CIC) This is a baseline approach. We predicted multi- criteria ratings independently and use them as context to predict the final overall rating
  • 13. 13 Experimental Setting • Data Sets • Evaluations – Five-fold Cross Validation – Rating Prediction: Mean Absolute Error (MAE) – Top-N Recommendation: Precision, Recall, NDCG – We use CAMF_C for context-aware recommendations User Item Rating # of Criteria TripAdvisor 1,502 14,300 22,130 7 Yahoo!Movie 2,162 3,075 49,351 4
  • 14. 14 Experimental Results The overall result demonstrates CCA and CCC outperform baselines, CCC is the best
  • 15. 15 Experimental Results Criteria Chains is able to improve the predictions on individual ratings on criterion
  • 16. 16 Experimental Results Information Gain is the best way to produce the optimal sequence
  • 17. 17 Conclusions and Future Work • Criteria Chains work better than baseline approaches • Criteria Chains take correlations among multiple criteria into consideration • Information Gain is the best way to produce chain • Using multi-criteria ratings as contexts, CCC, is the best approach after predictions on multiple ratings • Future Work: figure out optimal ways to generate the chain sequence in addition to information gain.
  • 18. Criteria Chains: A Novel Multi-Criteria Recommendation Approach Yong Zheng Illinois Institute of Technology Chicago, IL, 60616, USA ACM Conference on Intelligent User Interfaces Limassol, Cyprus, March 13-16, 2017