Providing Justifications in Recommender Systems

1. Providing Justifications in Recommender Systems Presenter : Keng-Yu Lin Author : PanagiotisSymeonidis, AlexandrosNanopoulos, and YannisManolopoulos IEEE. 2008

2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

3. Motivation Existing recommended systems miss the interaction between the user and his favorite features that can be used for justifying a recommendation. Existing recommended systems cannot detect partial matching of the user’s preferences. Existing recommended systems lack metrics to evaluate the quality of justifications.

4. Objectives • This paper propose a novel approach to solve aforementioned problem.

5. Methodology This paper propose to capture the interaction between users and their favorite features by constructing a feature profile.

6. Methodology xMotif algorithm Creating groups of users

7. Methodology Weighted user-feature matrix

8. Methodology Mango iPhone Android U={u1,u2}, I={HTC,Transformer} Neighborhood formation

9. Methodology Item I7 is recommended, because it contains feature f1, which is included in item I1 you have rated. sum W(I1)=1 W(I7)=2+1=3 fr(f1)=2 fr(f3)=1 Generating the recommendation and justification lists

10. Methodology Coverage=((1+1+2+2) / (1+3+3+3))*100%=60% Coverage

11. Experiments Precision and explain coverage of FWNB versus a for (a) MovieLensand (b) Reuters data sets.

12. Experiments Comparison between SF, CFCB, and FWNB in terms of explain coverage versus N for (a) MovieLens and (b) Reuters data sets.

13. Experiments Comparison between IB, CFCB, and FWNB in terms of precision versus recall for (a) MovieLens and (b) Reuters data sets.

14. Experiments Result of user survey

15. Conclusions This paper propose an approach to attain both accurate and justifiable recommendations.

16. Comments • Advantage • Applications • Recommended systems