1. Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Utilizing Marginal Net Utility for Recommendation in E-commerce Author ： Jian Wang, Yi Zhang Presented ： Fen-Rou Ciou ACM, 2011

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outlines  Motivation  Objectives  Methodology  Experiments  Conclusions  Comments

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Motivation  To better match users’ purchase decision in the real world.  Most of existing recommendation algorithms has three disadvantages. ─ Marginal net utility optimization ─ Cannot model the above two different products well. ─ Highest predicted ratings to recommend.

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objectives  This paper use marginal net utility to develop recommendation algorithms.  The new function contains a factor to control the product’s marginal utility diminishing rate. Marginal net utility

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology New marginal utility function 5

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology New marginal net utility function 6

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Apply new marginal utility function on SVD 7

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 8

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 9

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 10

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 11

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 12 Conclusions  On shop.com data, the new methods perform significantly better than baselines.  performs better in the re-purchase product recommendation task.  is more useful in recommending new products

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 13 Comments  Advantages ─.  Applications ─ Recommender System, Consumer Utility Function