Collaborative filtering analyzes data from multiple sources to develop profiles of people with similar tastes. It finds similarities between users based on their preferences and provides recommendations based on the preferences of similar users. Collaborative filtering requires a large amount of stored user data to make reliable recommendations, and the more users in the population, the more useful the recommendations will be. However, with small datasets it can produce false connections or poor predictions.

1. Collaborative FilteringZaffar Ahmed

2. Overview It analyzes data which relies on using data from numerous sources to develop profiles of people who are related with similar tastes and spending habits. It is based on ‘word-of-mouth’ idea Gives reliable recommendations

3. Mechanism behind Collaborative Filtering Users preferences are registered Similarity metric vector is used and users are found whose preferences are similar A weighted average of preferences is calculated Resulting preference function is used for recommendations

4. Facts It needs a lot of stored data for reliable recommendations for the active user. Bigger population – more useful and effective recommendations will be produced (Smart Mobs) Small data – shows false connections or poor predictions of active user tastes Suffers from cold start problem – database needs to be populated first.

5. Types of Collaborative Filtering Memory-based: uses user rating data to compute similarity between users or items Neighborhood-based CF calculates similarity b/w two users or items, produces a prediction for the active user taking the weighted average of all the ratings. Item/user based top-N recommendations identifies the K most similar users using similarity based vector model. Locality sensing hashing: It implements nearest neighbor mechanism in linear time. Advantages: explainability of the results, 2) easy to create and use, 3) new data can be added easily and incrementally Disadvantages: 1) depends on human rating, 2) performance decreases when data gets sparse, 3) it can not handle new users or items

6. Types of Collaborative Filtering Model-based: models (ontologies) are developed using data mining, machine learning algorithms to find patterns based on training data. It has more holistic goal to uncover latent factors that explain observed ratings. Bayesian Networks Clustering models Latent semantic models Advantages Handles sparsity better than memory based algos: improves scalability and prediction performance. Disadvantages Expensive model building

7. Types of Collaborative Filtering Hybrid Combines model-based and memory-based CF algos. overcomes the limitations of native CF approaches. Advantages Improves prediction performance Disadvantages Increased complexity Expensive to implement

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