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| Content Provider | ACM Digital Library |
|---|---|
| Author | Zhang, Qing Wang, Houfeng |
| Abstract | With a large amount of complex network data available, most existing recommendation models consider exploiting rich user social relations for better interest targeting. In these approaches, the underlying assumption is that similar users in social networks would prefer similar items. However, in practical scenarios, social link may not be formed by common interest. For example, one general collected social network might be used for various specific recommendation scenarios. The problem of noisy social relations without interest relevance will arise to hurt the performance. Moreover, the sparsity problem of social network makes it much more challenging, due to the two-fold problem needed to be solved simultaneously, for effectively incorporating social information to benefit recommendation. To address this challenge, we propose an adaptive embedding approach to solve the both jointly for better recommendation in real world setting. Experiments conducted on real world datasets show that our approach outperforms current methods. |
| Starting Page | 917 |
| Ending Page | 920 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450340694 |
| DOI | 10.1145/2911451.2914740 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-07-07 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Collaborative filtering Social personalized ranking Embedding Recommendation |
| Content Type | Text |
| Resource Type | Article |
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