This thesis proposal aims to develop a new similarity computation method for collaborative recommender systems that incorporates the use of trust between users. The proposed method will have three phases: 1) constructing neighborhoods of similar users using Pearson correlation and Jaccard similarity, 2) determining trust values for neighborhood members based on similarity, confidence, and profile trust measurements, and 3) combining phase 1 and 2 to define trusted neighbors and calculate actual similarities. The goal is to improve system accuracy by leveraging trust relationships. A literature review discusses existing trust-based recommendation approaches.

1. THESIS PROPOSAL
Falguni Roy
MSSE- 0209
10/24/2014 1

2. New similarity computation using trust in
user based collaborative recommender
system
Supervised by
Sheikh Muhammad Sarwar
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3. Contents
• About Recommender System
• Motivation
• Research Question
• Challenge
• Literature Review
• Proposed Method
• Tentative Timeline
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5. Recommender System
• Most popular forms of web information
customization system
• Used in E-commerce and entertainment based
websites
• To predict the 'rating' or 'preference' that user would
give to an item
• Approaches
– Collaborative filtering
– Content-based filtering
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6. Content-Based Filtering
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7. Collaborative Filtering(CF)
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8. Category of CF
• Based on Methodology
• Model Based Method
– item recommendation by developing a model
– regression, Bayesian network, rule-based and
clustering
• Memory Based Method
– a rating matrix
– some statistical techniques applied on the rating
matrix.
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9. • Based on Similarity
Category of CF (Con’t)
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10. Motivation
Data
Sparsity
Trust
Cold
Start
Users
Cold Start
Items
Shilling
Attack
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11. Research Question?
• How to use trust that will be able to perform
efficiently and improve systems accuracy???
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12. Challenge
• How to define trust ?
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13. Literature Review
• Zhimin Chen et al [1]
• mean squared error and evaluation accuracy matrix
• Mohsen Jamali et al. [2] and Qusai Shambour et
al [3]
• social trust network
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14. Proposed Methodology
• Proposed methodology contains three phases:
• Phase 1 : Constructing a neighborhood of similar
users
• Phase 2 : Determining Trust value for all
neighborhood members
• Phase 3 : Similarity computation
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15. Proposed Methodology
(Con’t)
• Constructing a neighborhood of similar users
– Probable neighborhood of similar minded
users
– Integration of Pearson Correlation Coefficient
(PCC) and Jaccard similarity method
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16. Proposed Methodology
(Con’t)
• Determining Trust value for all neighborhood
members
– Three components used to evaluate trust
– Components are:
• MSD: measure the degree of similarity between users
• Confidence factor: determine the confidence of target
user on the neighbor’s rating
• Profile trust measurement: verifies neighbor’s ratings
items and their effects on the system
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17. Proposed Methodology
• Similarity computation
– Combine phase 1 & 2 values
– Define actual trusted neighbors
(Con’t)
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18. Workflow
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19. Tentative Timeline
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20. References
[1]Zhimin Chen, Yi Jiang, Yao Zhao, “A Collaborative Filtering
Recommendation Algorithm Based on User Interest Change and Trust
Evaluation”, JDCTA,volume: 4, number: 9, pages: 106—113,2010
[2] Mohsen Jamali, Martin Ester “TrustWalker: A Random Walk Model for
Combining Trust-based and Item-based Recommendation”, booktitle:
Proceedings of the 15th ACM SIGKDD international conference on
Knowledge discovery and data mining, pages: 397—406, 2009
[3] QusaiShambour, Jie Lu, “A trust-semantic fusion-based recommendation
approach for e-business applications”, Decision Support Systems,
volume: 54, number: 1, pages: 768—780, 2012
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