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Abstract
Session-based recommendation aims to predict a user’s next action based on a series of anonymous sequences and plays an essential role in various online applications, such as e-commerce and music applications. Recently, transformer-based models have obtained results that are competitive with or even surpass those of recurrent neural networks, because of the good performance of transformer models in capturing long-distance dependencies. However, a transformer has a limited ability to mine local contextual information, which can be regarded as collective features. Researchers are seeking to address this limitation by augmenting the contextual transition to boost session representation learning. Accordingly, in this paper, we enhance the capabilities of a transformer in a session-based recommendation task by introducing convolutional neural networks (CNNs) at the stage of aggregating the item features with long- and short-distance dependencies. We first borrow a self-attention module from the classic transformer model to explore the long-distance dependencies. We next propose horizontal and vertical convolutions for enhancing the local collective information and then obtain a session representation by integrating the two types of features. Extensive experiments on real-world datasets show that our method outperforms those that rely on a transformer or a CNN alone.
Original language | English |
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Pages (from-to) | 21-33 |
Number of pages | 13 |
Journal | Neurocomputing |
Volume | 531 |
Early online date | 11 Feb 2023 |
DOIs | |
Publication status | Published - 28 Apr 2023 |
Bibliographical note
Publisher Copyright:© 2023
Funding
The research described in this article was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS16/E01/19), the Direct Grant (DR22A2), Lam Woo Research Fund (LWP20019), and Faculty Research Grants (DB22B4 and DB22B7) from Lingnan University, Hong Kong, and a Hong Kong Metropolitan University research grant (PFDS/2022/03).
Keywords
- Convolutional neural network
- Recommender system
- Session-based recommendation
- Transformer
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Dive into the research topics of 'A Transformer–Convolution Model for Enhanced Session-Based Recommendation'. Together they form a unique fingerprint.Projects
- 3 Finished
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Modeling Bitcoin Transaction Network via Structural Identity Representation
XIE, H. (PI) & DAI, H. H. (CoI)
1/07/22 → 30/06/23
Project: Grant Research
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Cluster-level Social Emotion Classification Across Domains
XIE, H. (PI)
1/03/22 → 28/02/23
Project: Grant Research
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Preliminary Study on Deep Learning Techniques for Learning Low-level Visions for All Seasons
XIE, H. (PI), LIAO, J. (CoI) & QIN, J. (CoI)
1/01/22 → 18/12/22
Project: Grant Research