Name
Abstract | ||
|---|---|---|
Mention as a key feature on social networks can break through the effect of structural trapping and expand the visibility of a message. Although existing works usually use rank learning as implementation strategy before performing mention recommendation, these approaches may interfere with the influening factor exploration and cause some biases. In this paper, we propose a novel Context-aware Mention recommendation model based on Probabilistic Matrix Factorization (CMPMF). This model considers four important mention contextual factors including topic relevance, mention affinity, user profile similarity and message semantic similarity to measure the relevance score from users and messages dimensions. We fuse these mention contextual factors in latent spaces into the framework of probabilistic matrix factorization to improve the performance of mention recommendation. Through evaluation on a real-world dataset from Weibo, the empirically study demonstrates the effectiveness of discovered mention contextual factors. We also observe that topic relevance and mention affinity play a much significant role in the mention recommendation task. The results demonstrate our proposed method outperforms the state-of-the-art algorithms. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-22741-8_18 | COMPUTATIONAL SCIENCE - ICCS 2019, PT II |
Keywords | Field | DocType |
Mention recommendation, Social network, Probabilistic Matrix Factorization, Contextual information | Semantic similarity,Probabilistic matrix factorization,Visibility,Contextual information,User profile,Social network,Information retrieval,Computer science,Recommendation model,Distributed computing | Conference |
Volume | ISSN | Citations |
11537 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Jiang | 1 | 11 | 4.54 |
| Zhigang Lu | 2 | 10 | 6.68 |
| Ning Li | 3 | 145 | 48.40 |
| Zelin Cui | 4 | 0 | 0.68 |