Recommendation Based on Users’ Long-Term and Short-Term Interests with Attention
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-10-17
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Recommendations based on user behavior sequences are becoming more and more common.
Some studies consider user behavior sequences as interests directly, ignoring the mining and representation of implicit features.
However, user behaviors contain a lot of information, such as consumption habits and dynamic preferences.
In order to better locate user interests, this paper proposes a Bi-GRU neural network with attention to model user’s long-term historical preferences and short-term consumption motivations.
First, a Bi-GRU network is established to solve the long-term dependence problem in sequences, and attention mechanism is introduced to capture user interest changes related to the target item.
Then, user’s short-term interaction trajectory based on self-attention is modeled to distinguish the importance of each potential feature.
Finally, combined with long-term and short-term interests, the next behavior is predicted.
We conducted extensive experiments on Amazon and MovieLens datasets.
The experimental results demonstrate that the proposed model outperforms current state-of-the-art models in Recall and NDCG indicators.
Especially in MovieLens dataset, compared with other RNN-based models, our proposed model improved at least 2.32% at Recall@20, which verifies the effectiveness of modeling long-term and short-term interest of users, respectively.
American Psychological Association (APA)
Tan, Qiaoqiao& Liu, Fang’ai. 2019. Recommendation Based on Users’ Long-Term and Short-Term Interests with Attention. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1197107
Modern Language Association (MLA)
Tan, Qiaoqiao& Liu, Fang’ai. Recommendation Based on Users’ Long-Term and Short-Term Interests with Attention. Mathematical Problems in Engineering No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1197107
American Medical Association (AMA)
Tan, Qiaoqiao& Liu, Fang’ai. Recommendation Based on Users’ Long-Term and Short-Term Interests with Attention. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1197107
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1197107