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Recommender Systems

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Published on

Based on:
Recommender Systems by Prem Melville & Vikas Sindhwani

Presented by:
Vijayindu Gamage
Udith Gunaratna
Pubudu Gunatilaka

Published in: Technology, Education
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Recommender Systems

  1. 1. Based on: Recommender Systems by Prem Melville & Vikas Sindhwani Presented by: Vijayindu Gamage Udith Gunaratna Pubudu Gunatilaka
  2. 2. LOGORecommender Systems
  3. 3. LOGORecommender Systems Structure of a Recommender System
  4. 4. LOGORecommender Systems Classification  Collaborative Filtering  Neighborhood-based Collaborative Filtering  Item-based Collaborative Filtering  Model-based Collaborative Filtering  Content Based Recommending  Hybrid Approaches
  5. 5. LOGORecommender Systems Neighborhood-based Collaborative Filtering Basic Steps  Assign a weight to all users with respect to similarity with the active user.  Select k users that have the highest similarity with the active user – (neighborhood)
  6. 6. LOGORecommender Systems  Compute a prediction from a weighted combination of the selected neighbors’ ratings.
  7. 7. LOGORecommender Systems Neighborhood-based CF - Problem LESS users … neighbors are EASY to find !
  8. 8. LOGORecommender Systems Neighborhood-based CF - Problem MANY users … neighbors are HARD to find !
  9. 9. LOGORecommender Systems Item-based Collaborative Filtering  Proposed in 2003  DOES NOT match similar users  DOES match similar items  Leads to faster online systems  Results in improved recommendations
  10. 10. LOGORecommender Systems Item-based Collaborative Filtering  Pearson correlation is used  Rating for item i for user a is predicted
  11. 11. LOGORecommender Systems More Extensions Highly correlated neighbors based on very few co-rated items Significance Weighting  multiply the similarity weight by a significance weighting factor  Default Voting  assume a default value for the rating for items that have not been explicitly rated  Inverse User Frequency  Universally loved/hated items are bad
  12. 12. LOGORecommender Systems Model-based Collaborative Filtering  Uses statistical models for predictions  Based on data mining and machine learning algorithms  Latent factor and Matrix factorization models have emerged as a state-of-the-art methodology  Netflix Prize competition
  13. 13. LOGORecommender Systems Content-based Recommending Pure collaborative filtering recommenders treat all users and items as atomic units Can make a better personalized recommendation by knowing more about a user or an item  Demographic information  Movie genres  Literary genres
  14. 14. LOGORecommender Systems Content-based Recommending User liked & Movie Genre Recommendation
  15. 15. LOGORecommender Systems Content-based Recommending Focused on recommending items with associated textual content 2 approaches  Treat as an Information Retrieval (IR) Task  Treat as a Classification Task
  16. 16. LOGORecommender Systems Hybrid Approaches Used to leverage the strengths of content-based and collaborative recommenders. Merging the list results to produce a final list. Content-boosted collaborative filtering
  17. 17. LOGORecommender Systems Evaluation Metrics Evaluation matrix is used to measure the quality of a recommender system. These systems are typical measured using predictive accuracy metrics 1. Mean Absolute Error (MAE) 2. Root Mean Squared Error (RMSE)
  18. 18. LOGORecommender Systems Mean Absolute Error 
  19. 19. LOGORecommender Systems Root Mean Squared Error (RMSE) 
  20. 20. LOGORecommender Systems Challenges and Limitations Sparsity Cold-Start Problem Fraud  push attacks  nuke attacks
  21. 21. LOGORecommender Systems Sparsity User ratings matrix is typically very sparse Effects collaborative filtering systems The problem system has a very high item- to user ratio. The system is in the initial stages of use. Solution - making assumptions about the data generation process
  22. 22. LOGORecommender Systems Cold-Start Problem New items and new users pose a significant challenge to recommender systems. New item problem – content-based approach to produce recommendations for all items, New user problem selecting items to be rated by a user so as to rapidly improve recommendation performance with the least user feedback
  23. 23. LOGORecommender Systems Fraud Push attacks  Increase the rating of their own products Nuke attacks  Lower the ratings of their competitors Item-based collaborative filtering is more robust to these attacks Content based methods are unaffected by profile injection attacks.
  24. 24. LOGORecommender Systems Content based or Collaborative filtering Advantages of CF over CB CF can perform in domains where there is not much content associated with items CF can also preform when content is difficult for a computer to analyze. CF system has the ability to provide serendipitous recommendations.

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