The main goal of reading paper is not just understanding it. Try to understand the key concept, but we need to get new ideas and research directions from the paper.
Paper information
- title: Inter-sequence Enhanced Framework for Personalized Sequential Recommendation
- authors: Liu, F., Liu, Q., Guo, W., Guo, H., Liu, W., Tang, R., ... & He, X.
- venue: arXiv preprint arXiv:2004.12118 (2020).
- pdf link: pdf link
- github: Not released yet
- abstract:
Modeling sequential correlation of users’ historical interactions is essential in sequential recommendation. However, the majority of the approaches mainly focus on modeling the intra-sequence item correlation within each individual sequence but neglect the inter-sequence item correlation across different user interaction sequences. Though several studies have been aware of this issue, their method is either simple or implicit. To make better use of such information, we propose an inter-sequence enhanced framework for the Sequential Recommendation (ISSR). In ISSR, both inter-sequence and intra-sequence item correlation are considered. Firstly, we equip graph neural networks in the inter-sequence correlation encoder to capture the high-order item correlation from the user-item bipartite graph and the item-item graph. Then, based on the inter-sequence correlation encoder, we build GRU network and attention network in the intra-sequence correlation encoder to model the item sequential correlation within each individual sequence and temporal dynamics for predicting users’ preferences over candidate items. Additionally, we conduct extensive experiments on three real-world datasets. The experimental results demonstrate the superiority of ISSR over many state-of-the-art methods and the effectiveness of the inter-sequence correlation encoder.
Summary: problems to address, key ideas, quick results
presentation link
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The main goal of reading paper is not just understanding it. Try to understand the key concept, but we need to get new ideas and research directions from the paper.
Paper information
Modeling sequential correlation of users’ historical interactions is essential in sequential recommendation. However, the majority of the approaches mainly focus on modeling the intra-sequence item correlation within each individual sequence but neglect the inter-sequence item correlation across different user interaction sequences. Though several studies have been aware of this issue, their method is either simple or implicit. To make better use of such information, we propose an inter-sequence enhanced framework for the Sequential Recommendation (ISSR). In ISSR, both inter-sequence and intra-sequence item correlation are considered. Firstly, we equip graph neural networks in the inter-sequence correlation encoder to capture the high-order item correlation from the user-item bipartite graph and the item-item graph. Then, based on the inter-sequence correlation encoder, we build GRU network and attention network in the intra-sequence correlation encoder to model the item sequential correlation within each individual sequence and temporal dynamics for predicting users’ preferences over candidate items. Additionally, we conduct extensive experiments on three real-world datasets. The experimental results demonstrate the superiority of ISSR over many state-of-the-art methods and the effectiveness of the inter-sequence correlation encoder.
Summary: problems to address, key ideas, quick results
presentation link
Questions about the paper?
What do you like?
What you don't like?
How to improve?
Any new ideas?
Reproducing results (if any)