A Probabilistic Approach to Personalized Tag Recommendation

1. A Probabilistic Approach to Personalized Tag Recommendation MeiqunHu, Ee-Peng Lim and Jing Jiang School of Information Systems Singapore Management University

2. Image credit @ logorunner.com Social Tagging • Social tagging allows users to annotate resources with tags. • organize • tags are keywords, serving as (personalized) index terms that group relevant resources • store • online storage gives mobility and convenience to access • share • published bookmarks can be viewed by other users • explore • to leverage collective wisdom to find interesting resources

3. Personalized Tag Recommendation • Personalized tag recommendation aims to recommend tags to the query user for annotating the query resource. • Recommendation eases the tagging process. ?

4. Why Personalize Recommendations? • Tag recommendation should be personalized. • users exhibit individualized choice of tag terms • e.g., language preference • personalized index for personal consumption and consistency

5. Problem Formulation and A Basic Method • Problem Formulation: p(t|rq,uq) • A Basic Method: freq-r, to recommend top frequent tags • assuming that the more people have used this tag, the more likely it will be used again • current state-of-the-art in many social tagging sites, e.g., • fails to personalize the recommendations for the query user

6. Three Scenarios Scenario 1: ‘foto’ is an infrequent tag for the resource. Scenario 2: ‘foto’ is has not been used for the resource, but has been used by the user for annotating other resources in the past. Scenario 3: ‘foto’ has not been used for the resource but has been used by others when annotating other resources.

7. Collaborative Filtering Method • A Method based on Collaborative Filtering: knn, to select top k-nearest neighbors and recommend tags used by these neighbors for annotating the resource • assuming that there are like-minded users who have annotated the same resource • classic collaborative filtering, without ratings • addresses scenario 1, but • fails scenario 2,3

8. Personomy Translation Method • To translate the resources tags to the user’s personal tags (trans-u) • to learn p(‘foto’|uq, ‘photo’) • addresses scenario 2, but • fails scenario 3, if uq has never used ‘foto’

9. To Address Scenario 3 borrow translation

10. Personomy Translation A Framework Measuring User Similarity A Probabilistic Framework

11. Borrowing Translations • To learn p(‘foto’|u,‘photo’) and sim(u,uq) borrow translation

12. Personomy Translation • To learn p(‘foto’|uq,‘photo’) [Wetzker et al. 2009]

13. Measuring Similarity between Users • sim(u,uq) • assuming that users are similar if they perform similar translations • users are profiled by sets of translation probabilities, e.g., p(‘foto’|u,‘photo’),…, p(‘image’|u,‘photo’) p(‘netz’|u,‘web’),…, p(‘internet’|u,‘web’) • we adopt distributional divergence to measure (dis)similarity between users • JS-divergence, L1-norm, such as in [Lee 1997]

14. Distributional Divergence between Users sim(‘photo’)(u,uq) S  sim(u,uq) … sim(‘web’)(u,uq)

15. Remark on the 3 Scenarios • This framework is able to address all three scenarios • addresses scenario 1 by allowing self-translation, e.g., p(‘photo’|u,‘photo’) • addresses scenario 2 by allowing self-similarity, e.g., sim(uq,uq) • addresses scenario 3 by enabling borrowed translations

16. Data Collection Experimental Setup Recommendation Performance Experiments

17. Dataset from BibSonomy Note: time order: train  validation  test users in test set must have been appeared in validation set.

18. Experimental Setup • Methods to compare • trans-n1, trans-n2 • k: {5,10,20,50,100,200,300,400,500} • js-divergence, l1-norm • b: {1,2,4,8} for js-divergence b: {1,2,4,8,12,16} for l1-norm • trans-u1, trans-u2 • knn-ur, knn-ut • k: {5,10,20,50,100,200,300,400,500} • interpolating with freq-r • Evaluation metric • pr-curve at top 5 • macro-average for users • Parameter optimization • macro-average f1@5 • global vs. individual settings

19. Recommendation PerformanceGlobal Setting

20. Recommendation PerformanceIndividual Setting

21. Recommendation Case Study scenario 3 tags

22. Conclusion • We propose a probabilistic framework for solving the personalized tag recommendation task, which incorporate personomy translation and borrowing translation from neighbors. • We devise to use distributional divergence to measure similarity between users. Users are similar if they exhibit similar translation behavior. • We find the proposed methods give superior performance than translation by the query user only and classic collaborative filtering.