Name
Abstract | ||
|---|---|---|
ABSTRACTPersonalized news recommendation aims to alleviate information overload and help users find news of their interests. Accurately matching candidate news and users' interests is the key to news recommendation. Most existing methods separately encode each user and news into vectors by news contents and then match the two vectors. However, a user's interest may differ in each news or each topic of one news. It's necessary to dynamically learn user and news vector and model their interaction. In this work, we present Recurrent Reasoning Memory Network over BERT (RMBERT) for news recommendation. Compared with other methods, our approach can leverage the ability of content modeling from BERT. Moreover, the recurrent reasoning memory network which performs a series of attention based reasoning steps can dynamically learn user and news vector and model their interaction in each step. As a result, our approach can better model user's interests. We conduct extensive experiments on a real-world news recommendation dataset and the results show that our approach significantly outperforms existing state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3404835.3463234 | Research and Development in Information Retrieval |
Keywords | DocType | Citations |
News Recommendation, Deep Neural Network, Attention Model | Conference | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qinglin Jia | 1 | 1 | 1.03 |
| Jingjie Li | 2 | 1 | 0.69 |
| Qi Zhang | 3 | 1 | 0.69 |
| Xiuqiang He | 4 | 312 | 39.21 |
| Jieming Zhu | 5 | 44 | 5.27 |