Multi-intent Aware Contrastive Learning for Sequential Recommendation

• URL: http://arxiv.org/abs/2409.08733v1
• Date: Fri, 13 Sep 2024 11:34:28 GMT
• Title: Multi-intent Aware Contrastive Learning for Sequential Recommendation
• Authors: Junshu Huang, Zi Long, Xianghua Fu, Yin Chen,
• Abstract summary: Intent is a significant latent factor influencing user-item interaction sequences. SR models considering multi-intent information in their framework are more likely to reflect real-life recommendation scenarios accurately.
• Score: 0.7187829059731446
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Intent is a significant latent factor influencing user-item interaction sequences. Prevalent sequence recommendation models that utilize contrastive learning predominantly rely on single-intent representations to direct the training process. However, this paradigm oversimplifies real-world recommendation scenarios, attempting to encapsulate the diversity of intents within the single-intent level representation. SR models considering multi-intent information in their framework are more likely to reflect real-life recommendation scenarios accurately.

Related papers

• Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation [69.60321475454843]
  We propose DPCPL, the first pre-training and prompt-tuning paradigm tailored for Multi-Behavior Sequential Recommendation. In the pre-training stage, we propose a novel Efficient Behavior Miner (EBM) to filter out the noise at multiple time scales. Subsequently, we propose to tune the pre-trained model in a highly efficient manner with the proposed Customized Prompt Learning (CPL) module.
  arXiv  Detail & Related papers  (2024-08-21T06:48:38Z)
• A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU [41.45522079026888]
  Multi-intent natural language understanding (NLU) presents a formidable challenge due to the model confusion arising from multiple intents within a single utterance. Previous works train the model contrastively to increase the margin between different multi-intent labels. We introduce a two-stage Prediction-Aware Contrastive Learning framework for multi-intent NLU.
  arXiv  Detail & Related papers  (2024-05-05T13:09:55Z)
• Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement [5.734747179463411]
  We propose a Contrastive Learning sequential recommendation method based on Multi-Intention Disentanglement (MIDCL) In our work, intentions are recognized as dynamic and diverse, and user behaviors are often driven by current multi-intentions. We propose two types of contrastive learning paradigms for finding the most relevant user's interactive intention, and maximizing the mutual information of positive sample pairs.
  arXiv  Detail & Related papers  (2024-04-28T15:13:36Z)
• Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation [30.715182718492244]
  This paper introduces a novel approach, Behavior-Contextualized Item Preference Modeling (BCIPM) for multi-behavior recommendation. Our proposed Behavior-Contextualized Item Preference Network discerns and learns users' specific item preferences within each behavior. It then considers only those preferences relevant to the target behavior for final recommendations, significantly reducing noise from auxiliary behaviors.
  arXiv  Detail & Related papers  (2024-04-28T12:46:36Z)
• Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation [0.0]
  We are the first to apply the Fisher-Merging method to Sequential Recommendation, addressing and resolving practical challenges associated with it. We demonstrate the effectiveness of our proposed methods, highlighting their potential to advance the state-of-the-art in sequential learning and recommendation systems.
  arXiv  Detail & Related papers  (2023-07-05T05:58:56Z)
• Towards Universal Sequence Representation Learning for Recommender Systems [98.02154164251846]
  We present a novel universal sequence representation learning approach, named UniSRec. The proposed approach utilizes the associated description text of items to learn transferable representations across different recommendation scenarios. Our approach can be effectively transferred to new recommendation domains or platforms in a parameter-efficient way.
  arXiv  Detail & Related papers  (2022-06-13T07:21:56Z)
• Intent Contrastive Learning for Sequential Recommendation [86.54439927038968]
  We introduce a latent variable to represent users' intents and learn the distribution function of the latent variable via clustering. We propose to leverage the learned intents into SR models via contrastive SSL, which maximizes the agreement between a view of sequence and its corresponding intent. Experiments conducted on four real-world datasets demonstrate the superiority of the proposed learning paradigm.
  arXiv  Detail & Related papers  (2022-02-05T09:24:13Z)
• Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning [68.45370492516531]
  We introduce Scalarized Multi-Objective Reinforcement Learning (SMORL) for the Recommender Systems (RS) setting. SMORL agent augments standard recommendation models with additional RL layers that enforce it to simultaneously satisfy three principal objectives: accuracy, diversity, and novelty of recommendations. Our experimental results on two real-world datasets reveal a substantial increase in aggregate diversity, a moderate increase in accuracy, reduced repetitiveness of recommendations, and demonstrate the importance of reinforcing diversity and novelty as complementary objectives.
  arXiv  Detail & Related papers  (2021-10-28T13:22:45Z)
• Self-Supervised Reinforcement Learning for Recommender Systems [77.38665506495553]
  We propose self-supervised reinforcement learning for sequential recommendation tasks. Our approach augments standard recommendation models with two output layers: one for self-supervised learning and the other for RL. Based on such an approach, we propose two frameworks namely Self-Supervised Q-learning(SQN) and Self-Supervised Actor-Critic(SAC)
  arXiv  Detail & Related papers  (2020-06-10T11:18:57Z)
• Sequential Recommendation with Self-Attentive Multi-Adversarial Network [101.25533520688654]
  We present a Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the effect of context information on sequential recommendation. Our framework is flexible to incorporate multiple kinds of factor information, and is able to trace how each factor contributes to the recommendation decision over time.
  arXiv  Detail & Related papers  (2020-05-21T12:28:59Z)

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.