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- 1. Utilizing Marginal Net Utility forRecommendation in E-commerceWUME Reading GroupLiangjie Hong
- 2. Overview• “Main Stream” RecSys Research• User Modeling in RecSys• Utilizing Marginal Net Utility for Recommendation in E-commerce Jian Wang and Yi Zhang, SIGIR 2011 Jian is a former WUME lab member.
- 3. “Main Stream” RecSys Research• Rating Prediction Item 1 Item 2 Item 3 Item 4 User 1 5 4 User 2 3 1 User 3 User 4 1 User 5 User 6 1 3 2
- 4. “Main Stream” RecSys Research• Top-K Prediction
- 5. “Main Stream” RecSys Research• Methods ▫ Neighborhood methods item-item user-user ▫ Content-based methods ▫ Latent factor models SVD Tensor ▫ Hybrid
- 6. “Main Stream” RecSys Research• Challenges ▫ How to incorporate meta-data/features? Regression-based models Matrix/Tensor co-factorization models
- 7. “Main Stream” RecSys Research• Challenges ▫ How to incorporate meta-data/features? Regression-based models Matrix/Tensor co-factorization models ▫ How to model user activities? Better performance? Insights on data?
- 8. “Main Stream” RecSys Research• Challenges ▫ How to incorporate meta-data/features? Regression-based models Matrix/Tensor co-factorization models ▫ How to model user activities? Better performance? Insights on data?
- 9. User Modeling• Long term ▫ Temporal Dynamics Collaborative filtering with temporal dynamics. KDD 2009 Yehuda Koren Temporal diversity in recommender systems. SIGIR 2010 Neal Lathia, Stephen Hailes, Licia Capra, Xavier Amatriain ▫ Users’ Motive ?
- 10. User Modeling• Long term ▫ Temporal Dynamics• Short term ▫ Session modeling Optimize rank measure? Collaborative ranking. WSDM 2012 Suhrid Balakrishnan, Sumit Chopra Click model? Collaborative competitive filtering: learning recommender using context of user choice. SIGIR 2011 Shuang-Hong Yang, Bo Long, Alexander J. Smola, Hongyuan Zha, Zhaohui Zheng
- 11. Interdisciplinary research• Economics Utilizing marginal net utility for recommendation in e-commerce. SIGIR 2011 Jian Wang, Yi Zhang An Economic Model of User Rating in an Online Recommender System. User Modeling 2005 F. Maxwell Harper, Sherry Xin Li, Yan Chen, Joseph A. Konstan• Control Theory Using control theory for stable and efficient recommender systems. WWW 2012 Tamas Jambor, Jun Wang, Neal Lathia
- 12. User Modeling• Long term ▫ Temporal Dynamics ▫ Users’ Motive• Short term ▫ Session modeling Optimize rank measure? Click model?
- 13. Utilizing Marginal Net Utility forRecommendation in E-commerceMain contribution:• Introduce the idea of utilizing the marginal net utility in RecSys• Apply the new utility function on SVD algorithm• New algorithm significantly outperforms baselines in a number of tasks
- 14. Total Utility, Marginal Utility
- 15. Total Utility, Marginal Utility
- 16. Total Utility, Marginal Utility
- 17. Total Utility, Marginal Utility
- 18. Total Utility, Marginal Utility Marginal Net Utility = Marginal Utility – PriceMarginal Utility is based on user’s previous purchase history.
- 19. Total Utility, Marginal Utility• Two types of products ▫ Not likely to be re-purchased in a short-time period e.g. iphone, laptop, digital camera … ▫ Likely to be re-purchased in a short-time period e.g., food, water, coffee …
- 20. Goal• Model users’ behavior based on marginal net utility• Make recommendations to maximize the marginal net utility for each user
- 21. Basic EconomicsLinear Utility Function•
- 22. Basic EconomicsLinear Utility Functionwhere and• is a marginal utility for the addition purchase of product i
- 23. Basic EconomicsLinear Utility Function• does not capture diminishing of return characteristic• most existing algorithms are implicitly based on this utility function
- 24. Basic EconomicsCobb-Douglas Utility
- 25. Basic EconomicsCobb-Douglas Utility
- 26. Basic EconomicsCobb-Douglas Utility• diminishing of return rate is fixed• basic utility is not personalized
- 27. Recommend based on Marginal Net Utility
- 28. Problem Setting
- 29. Design the Utility Functional Form
- 30. Design the Utility Functional Form only same product will influence the marginal utility
- 31. Revamp Existing Algorithms
- 32. Revamp Existing Algorithms• f(u,i) encodes the value of product i to user u without considering diminishing return and the cost of product i.• f(u,i) can be estimated through any methods.
- 33. Revamp Existing Algorithms• f(u,i) encodes the value of product i to user u without considering diminishing return and the cost of product i.• f(u,i) can be estimated through any methods.• f(u,i) is from SVD.
- 34. Revamp Existing Algorithms
- 35. Revamp Existing Algorithms
- 36. Learning Parameters if user u purchases i at time t. Otherwise -1.
- 37. Learning Parameters• Maximum A Posterior (MAP) estimation• Solved by stochastic gradient descent
- 38. Experiments• More than 5-years purchase history from 2004- 01-01 to 2009-03-08• 10,399 users, 65,551 products, and 102,915 unique (user, product) pairs• 80% training, 10% validation, and 10% testing
- 39. Experiments•
- 40. Experiments
- 41. Experiments• Two tasks ▫ Recommend to re-purchase e.g., 13.79% of orders contain re-purchased products ▫ Recommend new products e.g., 90.64% of orders contain new products
- 42. Experiments
- 43. Experiments Simply count does not work !
- 44. Experiments Simply count does not work !
- 45. Experiments
- 46. Experiments
- 47. Experiments
- 48. Conclusion• Introduce a new framework for recommender system in e-commerce sites• Recommend products with the highest marginal net utility• Take SVD as an example to revamp• Achieve significant improvement in the conversion rate

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