Name
Abstract | ||
|---|---|---|
Recommender systems have become indispensable for recommending items of interest to users and have been successfully deployed in a wide range of real-world applications. In this paper, we exploit the community influence of users in social networks to improve the recommendation accuracy. Depending on the community in which the user is located, the user's preferences are defined more accurately. Specifically, we propose a community-based collaborative filtering method for social recommender systems, which makes full use of the rich link/community structure within a social network. We first group users in a social network into overlapping communities. Then we explicitly incorporate the community preference into the latent factor model. Compared with seven state-of-the-art methods on four real-world datasets, our method achieves the best performance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICWS.2019.00036 | 2019 IEEE International Conference on Web Services (ICWS) |
Keywords | DocType | ISBN |
Recommender systems | Conference | 978-1-7281-2718-7 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| bin liang | 1 | 20 | 5.19 |
| Bo Xu | 2 | 25 | 5.31 |
| Xiaowei Wu | 3 | 9 | 0.80 |
| Dong Wu | 4 | 2 | 1.37 |
| Deqing Yang | 5 | 29 | 9.69 |
| Yanghua Xiao | 6 | 482 | 54.90 |
| Wei Wang | 7 | 7122 | 746.33 |