End-to-End Graph-Sequential Representation Learning for Accurate Recommendations

• URL: http://arxiv.org/abs/2403.00895v3
• Date: Fri, 15 Mar 2024 00:40:50 GMT
• Title: End-to-End Graph-Sequential Representation Learning for Accurate Recommendations
• Authors: Vladimir Baikalov, Evgeny Frolov,
• Abstract summary: This paper presents a novel multi-representational learning framework exploiting these two paradigms' synergies. Our empirical evaluation on several datasets demonstrates that mutual training of sequential and graph components with the proposed framework significantly improves recommendations performance.
• Score: 0.7673339435080445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent recommender system advancements have focused on developing sequence-based and graph-based approaches. Both approaches proved useful in modeling intricate relationships within behavioral data, leading to promising outcomes in personalized ranking and next-item recommendation tasks while maintaining good scalability. However, they capture very different signals from data. While the former approach represents users directly through ordered interactions with recent items, the latter aims to capture indirect dependencies across the interactions graph. This paper presents a novel multi-representational learning framework exploiting these two paradigms' synergies. Our empirical evaluation on several datasets demonstrates that mutual training of sequential and graph components with the proposed framework significantly improves recommendations performance.

Related papers

• Semantic-Enhanced Relational Metric Learning for Recommender Systems [27.330164862413184]
  Recently, metric learning methods have been received great attention in recommendation community, which is inspired by the translation mechanism in knowledge graph. We propose a joint Semantic-Enhanced Metric Learning framework to tackle the problem in recommender systems. Specifically the semantic signal is first extracted from the target reviews containing abundant features and personalized user preferences. A novel regression model is then designed via leveraging the extracted semantic signal to improve the discriminative ability of original relation-based training process.
  arXiv  Detail & Related papers  (2024-06-07T11:54:50Z)
• Dual-Channel Multiplex Graph Neural Networks for Recommendation [41.834188809480956]
  We introduce a novel recommendation framework, Dual-Channel Multiplex Graph Neural Network (DCMGNN) It incorporates an explicit behavior pattern representation learner to capture the behavior patterns composed of multiplex user-item interaction relations. It also includes a relation chain representation learning and a relation chain-aware encoder to discover the impact of various auxiliary relations on the target relation.
  arXiv  Detail & Related papers  (2024-03-18T09:56:00Z)
• GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation [58.6450834556133]
  We propose graph contrastive learning to enhance item representations with complex associations from the global view. We extend the CapsNet module with the elaborately introduced target-attention mechanism to derive users' dynamic preferences. Our proposed GUESR could not only achieve significant improvements but also could be regarded as a general enhancement strategy.
  arXiv  Detail & Related papers  (2023-03-01T05:46:36Z)
• Enhancing Sequential Recommendation with Graph Contrastive Learning [64.05023449355036]
  This paper proposes a novel sequential recommendation framework, namely Graph Contrastive Learning for Sequential Recommendation (GCL4SR) GCL4SR employs a Weighted Item Transition Graph (WITG), built based on interaction sequences of all users, to provide global context information for each interaction and weaken the noise information in the sequence data. Experiments on real-world datasets demonstrate that GCL4SR consistently outperforms state-of-the-art sequential recommendation methods.
  arXiv  Detail & Related papers  (2022-05-30T03:53:31Z)
• Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation [57.67616822888859]
  We propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging Reinforcement Learning (TMER-RL) TMER-RL utilizes reinforcement item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendation. Extensive evaluations of TMER on two real-world datasets show state-of-the-art performance compared against recent strong baselines.
  arXiv  Detail & Related papers  (2021-11-24T04:34:26Z)
• Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation [62.30552176649873]
  We devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe) To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice. To enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices.
  arXiv  Detail & Related papers  (2021-09-24T07:44:27Z)
• Contrastive Self-supervised Sequential Recommendation with Robust Augmentation [101.25762166231904]
  Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. Old and new issues remain, including data-sparsity and noisy data. We propose Contrastive Self-Supervised Learning for sequential Recommendation (CoSeRec)
  arXiv  Detail & Related papers  (2021-08-14T07:15:25Z)
• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization [104.87483578308526]
  We propose the model S3-Rec, which stands for Self-Supervised learning for Sequential Recommendation. For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-18T11:44:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.