Name
Abstract | ||
|---|---|---|
Context-aware features have been widely recognized as important factors in recommender systems. However, as a major technique in recommender systems, traditional Collaborative Filtering (CF) does not provide a straight-forward way of integrating the context-aware information into personal recommendation. We propose a Coupled Collaborative Filtering (CCF) model to measure the contextual information and use it to improve recommendations. In the proposed approach, coupled similarity computation is designed to be calculated by inter item, intra-context and inter-context interactions among item, user and context-ware factors. Experiments based on different types of CF models demonstrate the effectiveness of our design. |
Year | Venue | Field |
|---|---|---|
2015 | PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | Recommender system,Contextual information,Collaborative filtering,Similarity computation,Information retrieval,Computer science,Artificial intelligence,Machine learning,Information filtering system |
DocType | Citations | PageRank |
Conference | 2 | 0.37 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinxin Jiang | 1 | 2 | 0.71 |
| Wei Liu | 2 | 468 | 37.36 |
| Longbing Cao | 3 | 2212 | 185.04 |
| Guodong Long | 4 | 655 | 47.27 |