Name
Abstract | ||
|---|---|---|
Collaborative filter has been widely and successfully applied in recommendation system. It typically associates a user with a group of like-minded users based on their preferences over all the items, and recommends to the user those items enjoyed by others in the group. Some previous studies have explored that there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items and subgroup analysis can get better accuracy. While, we find that geographical information of user have impacts on user group preference for items. Hence, In this paper, we propose a Bayesian generative model to describe the generative process of user-item subgroup preference under considering users’ geographical information. Experimental results show the superiority of the proposed model. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.procs.2014.05.036 | Procedia Computer Science |
Keywords | Field | DocType |
Recommendation system,collaborative filter | Recommender system,Data mining,Collaborative filtering,Information retrieval,Computer science,Subgroup analysis,Artificial intelligence,Generative grammar,Machine learning,Generative model,Bayesian probability | Conference |
Volume | ISSN | Citations |
29 | 1877-0509 | 2 |
PageRank | References | Authors |
0.36 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhi Qiao | 1 | 43 | 8.60 |
| Peng Zhang | 2 | 478 | 39.61 |
| Ya-nan Cao | 3 | 131 | 19.42 |
| Chuan Zhou | 4 | 294 | 31.08 |
| Li Guo | 5 | 768 | 60.81 |