Name
Abstract | ||
|---|---|---|
Due to the data sparsity problem, social network information is often additionally used to improve the performance of recommender system. While most existing works exploit social information to reduce the rating prediction error, e.g., RMSE, a few had aimed to improve the top-k ranking prediction accuracy. This paper proposes a novel top-k oriented recommendation method, TRecSo, which incorporates social information into recommendation by modeling two different roles of users as trusters and trustees while considering the structural information of the network. Empirical studies on real-world datasets demonstrate that TRecSo leads to remarkable improvement compared to previous methods in top-k recommendation. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2872518.2889362 | WWW (Companion Volume) |
Field | DocType | Citations |
Recommender system,Learning to rank,Data mining,World Wide Web,Social network,Information retrieval,Ranking,Computer science,Mean squared error,Exploit,Social information,Empirical research | Conference | 7 |
PageRank | References | Authors |
0.44 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chanyoung Park | 1 | 163 | 12.04 |
| Dong Hyun Kim | 2 | 164 | 7.55 |
| Jinoh Oh | 3 | 303 | 15.32 |
| Hwanjo Yu | 4 | 1715 | 114.02 |