Name
Abstract | ||
|---|---|---|
Social tagging is one of the most important characteristics of web 2.0 services. Different from traditional recommendation algorithms, in social tagging systems, recommendation algorithms involve the ternary relations between users, items and tags. And algorithms that support integrated multi-mode recommendations are very appealing. We propose a multi-mode recommendation algorithm based on higher-order singular value decomposition, and our algorithm handles not only the existing triplets {user, item, tag}, but also the pairs {user, item} with no tags in social tagging system. Meanwhile. We propose a measure for user recommendations. We empirically show that our algorithm outperforms a state-of-the-art algorithm for multi-mode recommendations with a Last.fm dataset. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CCIS.2012.6664264 | Proceedings - 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems, IEEE CCIS 2012 |
Keywords | DocType | Volume |
tensor factorization,multi-recommendation,web services,multimode recommendation algorithms,social tagging systems,recommender systems,ternary relations,web 2.0 services,social networking (online),singular value decomposition,last.fm dataset,probability,higher-order singular value decomposition | Conference | 2 |
Issue | ISSN | ISBN |
null | null | 978-1-4673-1855-6 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tan Yang | 1 | 23 | 10.97 |
| Cui Yidong | 2 | 9 | 6.35 |
| Yuehui Jin | 3 | 5 | 1.51 |
| Maoqiang Song | 4 | 0 | 0.34 |