Learning Discriminative Recommendation Systems with Side Information

  title={Learning Discriminative Recommendation Systems with Side Information},
  author={Feipeng Zhao and Yuhong Guo},
Top-N recommendation systems are useful in many real world applications such as E-commerce platforms. Most previous methods produce top-N recommendations based on the observed user purchase or recommendation activities. Recently, it has been noticed that side information that describes the items can be produced from auxiliary sources and help to improve the performance of top-N recommendation systems; e.g., side information of the items can be collected from the item reviews. In this paper, we… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.

Explore Further: Topics Discussed in This Paper


Publications citing this paper.


Publications referenced by this paper.
Showing 1-10 of 36 references


Prateek Jain, Inderjit S. Dhillon. Provable inductive matrix completion
abs/1306.0626, • 2013
View 8 Excerpts
Highly Influenced

Acm Tois

Mukund Deshpande, George Karypis. Item-based top-N recommendation algorithms
22(1):143–177, • 2004
View 6 Excerpts
Highly Influenced

SLIM: Sparse Linear Methods for Top-N Recommender Systems

2011 IEEE 11th International Conference on Data Mining • 2011
View 4 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…