Name
Abstract | ||
|---|---|---|
Deep neural networks (DNNs) have been widely employed in recommender systems including incorporating attention mechanism for performance improvement. However, most of existing attention-based models only apply item-level attention on user side, restricting the further enhancement of recommendation performance. In this paper, we propose a knowledge-enhanced recommendation model ACAM, which incorporates item attributes distilled from knowledge graphs (KGs) as side information, and is built with a co-attention mechanism on attribute-level to achieve performance gains. Specifically, each user and item in ACAM are represented by a set of attribute embeddings at first. Then, user representations and item representations are augmented simultaneously through capturing the correlations between different attributes by a co-attention module. Our extensive experiments over two realistic datasets show that the user representations and item representations augmented by attribute-level co-attention gain ACAM's superiority over the state-of-the-art deep models.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3397271.3401313 | SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval
Virtual Event
China
July, 2020 |
DocType | ISSN | ISBN |
Conference | SIGIR 2020 | 978-1-4503-8016-4 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Deqing Yang | 1 | 29 | 9.69 |
| Zengcun Song | 2 | 0 | 0.34 |
| Lvxin Xue | 3 | 0 | 0.34 |
| Yanghua Xiao | 4 | 482 | 54.90 |