1. Online Evolutionary Collaborative Filtering RECSYS 2010 Intelligent Database Systems Lab. School of Computer Science & Engineering Seoul National University Center for E-Business Technology Seoul National University Seoul, Korea Presented by Sung Eun, Park 3/25/2011 Nathan N. Liu, Min Zhao, Evan Xiang, Qiang Yang Hong Kong University of Science and Technology, Hong Kong,

2. Copyright  2010 by CEBT Contents  Introduction  Evolutionary Collaborative Filtering  Online evolutionary Collaborative Filtering Incremental Similarity Computation  Experiments  Conclusion 2

3. Copyright  2010 by CEBT Introduction  User’s preference changes over a long period of time  Online evolutionary collaborative filtering Tracks user interests over time in order to make timely recommendations  Extension of neighborhood based algorithms 3 user1 user2 PopPop JazzJazzClassic PopPop JazzJazzClassic

4. Copyright  2010 by CEBT Typical Item-based Collaborative Filtering 1. Similarity Computation: Compute the item-item similarities (Cosine Similarity) 2. Neighborhood Computation: Find the most similar k-items 3. Score Prediction: Predict the unobserved ratings 4 For all users who rated both i and j … 3324 435 11 … 22 For all similar items

5. Copyright  2010 by CEBT Evolutionary Collaborative Filtering  Temporal Relevance Weight of rating on item i of user u at time t on parameter α Should decrease with the amount of time that has passed Based on the assumption that older ratings are generally less correlated with a user’s current interests or an item’s current characteristic A time gab between the current time and the time user rated the item i Emphasizing currently rated items

6. Copyright  2010 by CEBT  Evolutionary Collaborative Filtering

7. Copyright  2010 by CEBT Evolutionary Collaborative Filtering  Similarity Computation More emphasis on the recent rating of both items Inclined to identify nearest neighbors user1 user2 user3

8. Copyright  2010 by CEBT Evolutionary Collaborative Filtering  user3

9. Copyright  2010 by CEBT  Incremental Similarity Computation The problem of efficiently updating the model as new data arrives over time in large volumes Online Evolutionary Collaborative Filtering where

10. Copyright  2010 by CEBT Online Evolutionary Collaborative Filtering  Incremental Similarity Computation A set of users who newly rated i in time step t

11. Copyright  2010 by CEBT Online Evolutionary Collaborative Filtering 

12. Copyright  2010 by CEBT Experiments  Dataset Analysis Early ratings were often high – probably because they are most voted by the most enthusiastic fans Slow increase over time – Very old movies that has watched were often classics and therefore more likely to receive high ratings The variance of users’ ratings tended to increase over time – Better to user old age users to catch a explicit preference Movie age User age

13. Copyright  2010 by CEBT  Dataset Analysis A user tended to rate many more movies when he joined and became less and less active over time Experiments Movie age user age

14. Copyright  2010 by CEBT Evaluation Measure  RMSE(Root mean square error) The rating prediction accuracy How close their predicted ratings are to the true ratings  MAP(Mean Average Precision) Choice Prediction

15. Copyright  2010 by CEBT Evaluation Results  Effect of temporal relevance weighting RMSE MAP 1. In the item-based algorithm, the predicted scores are obtained by averaging a target user’s very few observed ratings 2. temporal relevance weighting’s effect to further reduce the contribution of old ratings would make the prediction less robust

16. Copyright  2010 by CEBT Evaluation Results  Effect of temporal relevance weighting Why are improvements on new items important? 1. the cosine similarity tends to favor old movies Old movies get more ratings and their cosine similarity with other movies tend to be higher 2. Reflects users’ current interests RMSE Movie Age Group User Age Group Better at new items Better at old users

17. Copyright  2010 by CEBT Evaluation Results  Effect of temporal relevance weighting  Better at old users Consistent with the intuition that it is more likely for the taste of old users to have drifted over time MAP Movie Age Group User Age Group

18. Copyright  2010 by CEBT Evaluation Results  Effect of incremental computation Incremental algorithm is 15 -20 times faster than the non incremental algorithm

19. Copyright  2010 by CEBT Conclusion  The use of temporal relevance weighting could lead to more significant improvements for the choice prediction task than for the rating prediction task.  A detailed analysis reveals that our algorithm can most effectively improve predictions for older users and newer items.  The proposed algorithm is simple and fast enough to cope with frequent data updates

20. Q&A Thank you 20