支持辅助语义学习的联合深度网络进行热门推荐
文献类型：期刊论文
| 作者 | 王星凯; 盛益强; 邓浩江 |
| 刊名 | IEEE Access
 |
| 出版日期 | 2020 |
| 期号 | 1页码:41254 |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2020.2976498 |
| 英文摘要 | There is a cold-start problem in the recommendation system field, which is how to profile new users and new items. The popular recommendation algorithm is an important solution to the coldstart problem. In this paper, we propose a new joint deep network model with auxiliary semantic learning for the popular recommendation algorithm (DMPRA). First, we define the items with a large quantity of review data and high ratings as the popular recommended items. Second, we introduce text analysis into the popular recommendation algorithm. We use the optimized CharCNN networks to learn the auxiliary semantic vectors from the users’ reviews. Then, we use the Factorization Machine (FM) component and deep component to learn the corresponding vector representations of the items’ attribute features. We use convolution to simulate the interaction of hidden latent vectors. This method can make the vectors interact more satisfactorily than traditional interactive representation methods. Finally, we provide the users with a reasonable popular recommendation list. The experimental results show that our algorithm can improve the AUC (area under the ROC curve) and Logloss (cross-entropy) of the popular items’ prediction. In addition, we provide relevant explanations for some useful phenomena. |
| URL标识 | 查看原文 |
| 源URL | [http://159.226.59.140/handle/311008/9517]  |
| 专题 | 历年期刊论文_2020年期刊论文 |
| 作者单位 | 中国科学院声学研究所 |
| 推荐引用方式 GB/T 7714 | 王星凯；盛益强；邓浩江. 支持辅助语义学习的联合深度网络进行热门推荐[J]. IEEE Access,2020(1):41254. |
| APA | 王星凯；盛益强；邓浩江.(2020).支持辅助语义学习的联合深度网络进行热门推荐.IEEE Access(1),41254. |
| MLA | 王星凯；盛益强；邓浩江."支持辅助语义学习的联合深度网络进行热门推荐".IEEE Access .1(2020):41254. |
入库方式： OAI收割
来源：声学研究所
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