Name
Title | ||
|---|---|---|
Modeling Long- and Short-Term User Behaviors for Sequential Recommendation with Deep Neural Networks |
Abstract | ||
|---|---|---|
In e-commerce platforms, a user's next behavior will be affected by his long-term constant interests and short-term temporal needs. Such information is usually hidden in the users' historical online behavior data, so how to capture long-term and short-term patterns becomes the key to design better recommendation models or algorithms. Current mainstream methods such as Markov chain, convolutional neural network, and recurrent neural network cannot well express the mixed dynamic characteristics. In this paper, we propose an attention-based deep neural network (ADNNet) to solve the problem. In ADNNet, a convolutional neural network is used to extract the short-term patterns in the behavior sequences, and a gated recurrent unit is used to mine the long-term patterns in the behavior sequences. The attention mechanism is adopted to help the network automatically learn the best fusion coefficient of these two patterns. Our experimental result on four real public datasets (+ 0.69% in Hit Ratio and +3.49% in MRR) shows the superiority of our proposed ADNNet compared with other state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9534103 | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
Keywords | DocType | ISSN |
sequential recommendation, self-attention, deep neural networks, convolutional neural network, gated recurrent unit | Conference | 2161-4393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cairong Yan | 1 | 0 | 1.35 |
| Yiwei Wang | 2 | 147 | 12.10 |
| Yanting Zhang | 3 | 3 | 2.80 |
| Zijian Wang | 4 | 2 | 1.72 |
| Pengwei Wang | 5 | 9 | 4.04 |