Name
Abstract | ||
|---|---|---|
Traditional recommender systems have achieved remarkable success. However, they only consider users' long-term interests, ignoring the situation when new users don't have any profile or user delete their tracking information. In order to solve this problem, the session-based recommendations based on Recurrent Neural Networks (RNN) is proposed to make recommendations taking only the behavior of users into account in a period time. The model showed promising improvements over traditional recommendation approaches. In this paper, We apply bidirectional long short-term memory (BLSTM) on movie recommender systems to deal with the above problems. Experiments on the MovieLens dataset demonstrate relative improvements over previously reported results on the Recall@N metrics respectively and generate more reliable and personalized movie recommendations when compared with the existing methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-51814-5_23 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Movie recommendation,Recommendation system,BLSTM,RNN | Recommender system,Pattern recognition,Computer science,Recurrent neural network,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | ISBN |
10133 | 0302-9743 | 9783319518138 |
Citations | PageRank | References |
2 | 0.37 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| song tang | 1 | 2 | 2.73 |
| Wu Zhiyong | 2 | 119 | 36.98 |
| Kang Chen | 3 | 536 | 37.47 |