Multi-level Contrastive Learning Framework for Sequential Recommendation
Zi-Jun Wang,Huoyu Liu,Wei Wei,Yue Hu,Xian-Ling Mao,Shaojian He,Rui Fang,Dangyang Chen +7 more
- 27 Aug 2022
TL;DR: The proposed MCLSR outperforms the state-of-the-art methods consistently and learns the representations of users and items through a cross-view contrastive learning paradigm from four specific views at two different levels (i.e., interest- and feature-level).
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Abstract: Sequential recommendation (SR) aims to predict the subsequent behaviors of users by understanding their successive historical behaviors. Recently, some methods for SR are devoted to alleviating the data sparsity problem (i.e., limited supervised signals for training), which take account of contrastive learning to incorporate self-supervised signals into SR. Despite their achievements, it is far from enough to learn informative user/item embeddings due to the inadequacy modeling of complex collaborative information and co-action information, such as user-item relation, user-user relation, and item-item relation. In this paper, we study the problem of SR and propose a novel multi-level contrastive learning framework for sequential recommendation, named MCLSR. Different from the previous contrastive learning-based methods for SR, MCLSR learns the representations of users and items through a cross-view contrastive learning paradigm from four specific views at two different levels (i.e., interest- and feature-level). Specifically, the interest-level contrastive mechanism jointly learns the collaborative information with the sequential transition patterns, and the feature-level contrastive mechanism re-observes the relation between users and items via capturing the co-action information (i.e., co-occurrence). Extensive experiments on four real-world datasets show that the proposed MCLSR outperforms the state-of-the-art methods consistently.
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Citations
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
TL;DR: TALLRec as mentioned in this paper proposes a fine-tuning framework for aligning large language models with recommendation data, which can significantly enhance the recommendation capabilities of LLMs in the movie and book domains.
Heterogeneous Graph Contrastive Learning for Recommendation
Mengru Chen,Chao Huang,Liang Xia,Wei Wei,Yong Xu,Ronghua Luo +5 more
- 27 Feb 2023
TL;DR: Wang et al. as mentioned in this paper proposed a Heterogeneous graph contrastive learning (HGCL) model, which is able to incorporate heterogeneous relational semantics into the user-item interaction modeling with contrastively learning-enhanced knowledge transfer across different views.
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
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- 14 Sep 2023
TL;DR: TALLRec is an effective and efficient tuning framework to align large language models with recommendation tasks. It significantly enhances the recommendation capabilities of LLMs and exhibits robust cross-domain generalization.
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Contrastive Self-supervised Learning in Recommender Systems: A Survey
TL;DR: A survey of contrastive self-supervised learning-based recommender systems can be found in this paper , where the authors provide an up-to-date and comprehensive review of the state-of-the-art.
Contrastive Self-supervised Learning in Recommender Systems: A Survey
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TL;DR: Contrastive self-supervised learning has emerged as a powerful technique for improving recommender systems by leveraging unlabeled data. This survey provides a comprehensive overview of current methods and offers guidance on choosing appropriate techniques based on key components.
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