Name
Abstract | ||
|---|---|---|
Movie recommendation systems provide users with ranked lists of movies based on individualu0027s 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 movies that are supposed to change slowly across time, session-based models encode the information of usersu0027 interests and changing dynamics of moviesu0027 attributes in short terms. In this paper, we propose an LSIC model, leveraging Long and Short-term Information in Content-aware movie recommendation using adversarial training. In the adversarial process, we train a generator as an agent of reinforcement learning which recommends the next movie to a user sequentially. We also train a discriminator which attempts to distinguish the generated list of movies from the real records. The poster information of movies is integrated to further improve the performance of movie recommendation, which is specifically essential when few ratings are available. The experiments demonstrate that the proposed model has robust superiority over competitors and sets the state-of-the-art. We will release the source code of this work after publication. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Information Retrieval | Recommender system,ENCODE,Discriminator,Adversarial process,Ranking,Information retrieval,Computer science,Source code,Adversarial system,Reinforcement learning |
DocType | Volume | Citations |
Journal | abs/1712.09059 | 1 |
PageRank | References | Authors |
0.34 | 20 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Zhao | 1 | 81 | 19.49 |
| Benyou Wang | 2 | 168 | 15.83 |
| J. Ye | 3 | 95 | 10.80 |
| Yongqiang Gao | 4 | 2 | 3.41 |
| Min Yang | 5 | 3 | 3.75 |
| Zhou Zhao | 6 | 773 | 90.87 |
| Xiaojun Chen | 7 | 14 | 6.41 |