Optimal Recommendations under Attraction, Aversion, and Social Influence
0 likes485 views
The document presents an interest evolution model that incorporates attraction, aversion, and social influence for making near-optimal recommendations in recommender systems. It introduces a semi-definite programming approach that significantly outperforms traditional matrix factorization methods, demonstrating the existence of attraction and aversion phenomena in real user data. The recommendations must consider the joint effects of various factors to maximize users' overall utility.
Optimal Recommendations under Attraction, Aversion, and Social Influence
- 1. Optimal Recommendationsunder Attraction, Aversion andSocial Influence Wei Lu (UBC) StratisIoannidis (Technicolor) SmritiBhagat(Technicolor) LaksV.S. Lakshmanan(UBC)
- 2. Users Engaging with Recommender Systems Attraction Aversion Influence… 2
- 3. Challenges •Making recommendations & modeling user interest are intertwined •Since recommendations play a role in changing user interest •Recommendations should be made in a global manner •Since social influence effect may trigger interest cascade 3
- 4. Main Contributions •An interest evolution model with attraction, aversion, and social influence •Use Semi-Definite Programming (SDP)to provide near-optimal recommendations •Outperform matrix factorization recommendations significantly •Show from real data that attraction and aversion phenomena do exist 4
- 5. Interest Evolution Model Inherent interests Social Influence Attracted to recommendations Aversive to recommendations At any time t, user selects social behavior with probability and personal behavior with probability Personal Social 1 − 훽 훽 훼푖 훾푖 훿푖 훼푖 + 훾푖 + 훿푖 = 1 푢푠푒푟 푖 5
- 6. Dynamic system of interest evolution 푣푖(푡) 푢푖(푡) Utility = <푢푖푡,푣푖푡> 6
- 7. Recommendation Problem What should the recommender’s strategy be, taking into account the joint effect of these factors so as to maximize users’ utility Recommendations to different users can’t be made in isolation 7
- 8. The evolution process is a Markov Chain It converges to a steady state Interest Evolution: Steady State social influence matrix expected inherent profile matrix expected item profile matrix prob. of attraction, aversion matrices prob. of inherent interest 8 Expected user profile matrix (1 row per user)
- 9. Recommendation Objective Multiply with Social welfare: Expected total utility over all users Global Recommendation Problem 9
- 10. • Variables to solve: • Quadratically-Constrained Quadratic Optimization Problem (QCQP) • Not convex, in general • Our solution strategy: Relaxation Global Recommendation 10
- 11. •Global Recommendation: Semi-Definite Program (SDP) with a rank-1 constraint •Relaxation: Drop rank-1 constraint SDP only •Solve the SDP relaxation •IF (rank-1): done! •ELSE: there exists a randomized algorithm giving a 4/7 approximation •Observation: In our experiments, all solution matrices are rank-1, hence optimal SDP Relaxation 11
- 12. Datasets Objectives •Find evidence of attraction and aversion from data •Evaluate SDP solutions •Baseline (MF-Local): Recommend based on inherent profiles only Experiments Flixster FilmTipSet MovieLens #users 4.6K 0.4K 8.9K #items 25K 4.3K 3.8K #ratings 1.8M 118K 1.3M #edges 44K N/A N/A 12
- 13. Heavy tail: existence of strongly attracted and aversive users Finding evidence of attraction & aversion 13 MovieLens
- 14. Incorporating evolution probabilities into Matrix Factorization leads to better predictions Finding evidence of attraction & aversion 14
- 15. •Synthetic SN: Forest-Fire, Kronecker, Power-law •Y-axis: 푆퐷푃푠표푙푢푡푖표푛−푏푎푠푒푙푖푛푒 푏푎푠푒푙푖푛푒 ∗100% Varying Social Effect $beta$ (Synthetic) 15
- 16. Social Welfare on Real Data SDP outperforms baseline on three real-world datasets, in terms of Social Welfare 16
- 17. •Study the optimality of SDP relaxation in theory •Improve scalability of SDP-based algorithms by exploiting special structural features •Study other factors for interest evolution Future Work 17
- 18. Thank you!