PLASTIC: Prioritize Long and Short-term Information in Top-n Recommendation using Adversarial Training
文献类型：会议论文
| 作者 | Wei Zhao; Benyou Wang; Jianbo Ye; Yongqiang Gao; Min Yang; Xiaojun Chen |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | Stockholm, Sweden |
| 英文摘要 | Recommender systems provide users with ranked lists of items based on individual’s preferences and constraints. Two types of models are commonly used to generate ranking results: long-term models and session-based models. While long-term models represent the interactions between users and items that are supposed to change slowly across time, session-based models encode the information of users’ interests and changing dynamics of items’ attributes in short terms. In this paper, we propose a PLASTIC model, Prioritizing Long And Short- Term Information in top-n reCommendation using adversarial training. In the adversarial process, we train a generator as an agent of reinforcement learning which recommends the next item to a user sequentially. We also train a discriminator which attempts to distinguish the generated list of items from the real list recorded. Extensive experiments show that our model exhibits significantly better performances on two widely used datasets. |
| 语种 | 英语 |
| URL标识 | 查看原文 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14086]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 推荐引用方式 GB/T 7714 | Wei Zhao,Benyou Wang,Jianbo Ye,et al. PLASTIC: Prioritize Long and Short-term Information in Top-n Recommendation using Adversarial Training[C]. 见:. Stockholm, Sweden. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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