Name
Abstract | ||
|---|---|---|
Recommender systems have been widely used in our life in recent years to facilitate our life. And it is very important and meaningful to improve recommendation performance. Generally, recommendation methods use users' historical ratings on items to predict ratings on their unrated items to make recommendations. However, with the increase of the number of users and items, the degree of data sparsity increases, and the quality of recommendations decreases sharply. In order to solve the sparsity problem, other auxiliary information is combined to mine users' preferences for higher recommendation quality. Similar to rating data, review data also contain rich information about users' preferences on items. This paper proposes a novel recommendation model, which harnesses an adversarial learning among auto-encoders to improve recommendation quality by minimizing the gap of the rating and review relation between a user and an item. The empirical studies on real-world datasets show that the proposed method improves the recommendation performance. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3233/WEB-210449 | WEB INTELLIGENCE |
Keywords | DocType | Volume |
Review, recommendation, adversarial learning, auto-encoder | Journal | 18 |
Issue | ISSN | Citations |
4 | 2405-6456 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jin Yi | 1 | 0 | 0.34 |
| Jiajin Huang | 2 | 69 | 15.70 |
| Jin Qin | 3 | 0 | 1.35 |