Zaffar+Ahmed+ +Collaborative+Filtering

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Zaffar+Ahmed+ +Collaborative+Filtering

  1. 1. Collaborative FilteringZaffar Ahmed<br />
  2. 2. Overview<br />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. <br />It is based on ‘word-of-mouth’ idea<br />Gives reliable recommendations<br />
  3. 3. Mechanism behind Collaborative Filtering<br />Users preferences are registered<br />Similarity metric vector is used and users are found whose preferences are similar<br />A weighted average of preferences is calculated<br />Resulting preference function is used for recommendations<br />
  4. 4. Facts<br />It needs a lot of stored data for reliable recommendations for the active user. <br />Bigger population – more useful and effective recommendations will be produced (Smart Mobs)<br />Small data – shows false connections or poor predictions of active user tastes<br />Suffers from cold start problem – database needs to be populated first.<br />
  5. 5. Types of Collaborative Filtering<br />Memory-based: uses user rating data to compute similarity between users or items<br />Neighborhood-based CF<br />calculates similarity b/w two users or items, produces a prediction for the active user taking the weighted average of all the ratings. <br />Item/user based top-N recommendations<br />identifies the K most similar users using similarity based vector model. <br />Locality sensing hashing: It implements nearest neighbor mechanism in linear time. <br />Advantages: <br />explainability of the results, 2) easy to create and use, 3) new data can be added easily and incrementally<br />Disadvantages: <br />1) depends on human rating, 2) performance decreases when data gets sparse, 3) it can not handle new users or items<br />
  6. 6. Types of Collaborative Filtering<br />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. <br />Bayesian Networks<br />Clustering models<br />Latent semantic models<br />Advantages<br />Handles sparsity better than memory based algos: improves scalability and prediction performance. <br />Disadvantages<br />Expensive model building<br />
  7. 7. Types of Collaborative Filtering<br />Hybrid <br />Combines model-based and memory-based CF algos.<br />overcomes the limitations of native CF approaches.<br />Advantages<br />Improves prediction performance<br />Disadvantages<br />Increased complexity<br />Expensive to implement<br />
  8. 8. Thank you<br />

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