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Utilizing Marginal Net Utility for Recommendation in E-commerce
1. Utilizing Marginal Net Utility for
Recommendation in E-commerce
WUME Reading Group
Liangjie 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
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 for
Recommendation in E-commerce
Main 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
18. Total Utility, Marginal Utility
Marginal Net Utility = Marginal Utility – Price
Marginal 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
23. Basic Economics
Linear Utility Function
• does not capture diminishing of return
characteristic
• most existing algorithms are implicitly based on
this utility function
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.
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
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
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