1. A Recommender System based on Tag and Time Information for Social Tagging Systems Nan Zheng and Qiudan Li (Chinese Academy of Sciences) Expert Systems with Applications, 2011 February 16, 2011 Hyunwoo Kim

2. Outline  Introduction  Proposed Approach  A Recommender System  Experimental Evaluation  Conclusion

3. Introduction 3 Tag Time - Interests of a user - Bridge between a user and a resource - First posting date - The latest posting date - Posting frequency

4. Introduction 4 Time?? Web Page - Page creation date

5. Introduction  Tags –Reflects the interest of a user as time goes by  For example, –Alice often uses baby health and education her bookmarks 5 in 2006 in 2011 Alice baby healtheducation

6. Introduction  A resource-recommendation model –Providing personalized services in social tagging systems –By three phases  Rating generation  User similarity calculation  Resource recommendation  Three strategies to generate ratings –Tag-weight strategy –Time-weight strategy –Tag and time strategy 6

7.  Two matrices –User-resource binary matrix  If a user has bookmarked a resource, the value is 1  Otherwise 0 –Modified user-resource rating matrix  Involving either tags, time, or both tag and time  Tag-weight: tag frequency of a user  Time-weight: time weight value  Tag and time: the value of the integration of tag weight value and time weight value Proposed Approach 7 100 110 011 User Resource 0.500 0.30.70 00.10.9 User Resource

8. Proposed Approach  The framework of resource-recommendation model 8

9. Proposed Approach - Rating Generation  Tag-weight strategy –Assumption  The more a tag has been used, the more interests the user has in the related resource  A user is likely to prefer the resources bookmarked with the high frequency tags –Tag weight is defined as tag(u,r) : the set of tags with which a user u has bookmarked to a resource r w u,t a : tag score of each tag t a in tag(u,r) 9

10. Proposed Approach - Rating Generation  Time-weight strategy –Assumption  Human interests drift as time goes by –To learn and track the changes of user’s behavior  Time window  Exponential forgetting function –Time weight defined as time(u,r) : non-negative integer. The value of 0 for the last tagging day and the value of 1 for the penultimate tagging day, and so on hl u : half-life for each user 10

11. Proposed Approach - Rating Generation  Tag and time strategy –Combining two weights into a single one –Tag and time weight defined as  Linear combination of tag weight and time weight –In order to denote user’s preference more accurately  Tags indicate user’s degree of preferences  Bookmarked time reflects interest drifts of a user 11

12. Proposed Approach  An example 12

13. A Recommender System  The architecture of the recommender system History browsing: tagging history browsing and tag browsing User network construction: network is constructed according to user similarity Resource recommendation: proposed model and log-based model 13

14. A Recommender System  Tag browsing 14

15. A Recommender System  Log-based model 15

16. A Recommender System  Proposed resource-recommendation model 16

17. Experimental Evaluation  Evaluation metrics –Hit-rate and hit-rank m : the total number of users h : the number of hits p i : positions 17

18. Experimental Evaluation  Tag’s impact 18

19. Experimental Evaluation  Time’s impact 19

20. Experimental Evaluation  Model with both tag and time information 20

21. Conclusion  In this paper –Proposing a resource-recommendation model to utilize tag and time information  Tag, time and both tag and time outperform traditional log-based model –Building a recommender system to provide personalized resource recommendation  Future work –Evaluating proposed resource-recommendation model with other datasets –Extending to social network analysis in social tagging systems 21

22. Thank You