Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation

• URL: http://arxiv.org/abs/2404.11993v1
• Date: Thu, 18 Apr 2024 08:39:52 GMT
• Title: Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation
• Authors: Shunpan Liang, Junjie Zhao, Chen Li, Yu Lei,
• Abstract summary: Multi-behavioral recommendation provides users with more accurate choices based on diverse behaviors, such as view, add to cart, and purchase. We propose a novel model: Knowledge-Aware Multi-Intent Contrastive Learning (KAMCL) model. This model uses relationships in the knowledge graph to construct intents, aiming to mine the connections between users' multi-behaviors from the perspective of intents to achieve more accurate recommendations.
• Score: 6.522900133742931
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-behavioral recommendation optimizes user experiences by providing users with more accurate choices based on their diverse behaviors, such as view, add to cart, and purchase. Current studies on multi-behavioral recommendation mainly explore the connections and differences between multi-behaviors from an implicit perspective. Specifically, they directly model those relations using black-box neural networks. In fact, users' interactions with items under different behaviors are driven by distinct intents. For instance, when users view products, they tend to pay greater attention to information such as ratings and brands. However, when it comes to the purchasing phase, users become more price-conscious. To tackle this challenge and data sparsity problem in the multi-behavioral recommendation, we propose a novel model: Knowledge-Aware Multi-Intent Contrastive Learning (KAMCL) model. This model uses relationships in the knowledge graph to construct intents, aiming to mine the connections between users' multi-behaviors from the perspective of intents to achieve more accurate recommendations. KAMCL is equipped with two contrastive learning schemes to alleviate the data scarcity problem and further enhance user representations. Extensive experiments on three real datasets demonstrate the superiority of our model.

Related papers

• GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation [13.1192216083304]
  We propose a novel Graphs and User Modalities Enhancement (GUME) for long-tail multimodal recommendation. Specifically, we first enhance the user-item graph using multimodal similarity between items. We then construct two types of user modalities: explicit interaction features and extended interest features.
  arXiv  Detail & Related papers  (2024-07-17T06:29:00Z)
• 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)
• Knowledge Enhancement for Multi-Behavior Contrastive Recommendation [39.50243004656453]
  We propose a Knowledge Enhancement Multi-Behavior Contrastive Learning Recommendation (KMCLR) framework. In this work, we design the multi-behavior learning module to extract users' personalized behavior information for user-embedding enhancement. In the optimization stage, we model the coarse-grained commonalities and the fine-grained differences between multi-behavior of users to further improve the recommendation effect.
  arXiv  Detail & Related papers  (2023-01-13T06:24:33Z)
• Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation [52.89816309759537]
  Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, etc.) widely exist in most real-world recommendation scenarios. The state-of-the-art multi-behavior models learn behavior dependencies indistinguishably with all historical interactions as input. We propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning framework to learn shared and behavior-specific interests for different behaviors.
  arXiv  Detail & Related papers  (2022-08-03T05:28:14Z)
• Multi-view Multi-behavior Contrastive Learning in Recommendation [52.42597422620091]
  Multi-behavior recommendation (MBR) aims to jointly consider multiple behaviors to improve the target behavior's performance. We propose a novel Multi-behavior Multi-view Contrastive Learning Recommendation framework.
  arXiv  Detail & Related papers  (2022-03-20T15:13:28Z)
• Contrastive Meta Learning with Behavior Multiplicity for Recommendation [42.15990960863924]
  A well-informed recommendation framework could not only help users identify their interested items, but also benefit the revenue of various online platforms. We propose Contrastive Meta Learning (CML) to maintain dedicated cross-type behavior dependency for different users. Our method consistently outperforms various state-of-the-art recommendation methods.
  arXiv  Detail & Related papers  (2022-02-17T08:51:24Z)
• Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation [56.12499090935242]
  This work proposes a Knowledge-Enhanced Hierarchical Graph Transformer Network (KHGT) to investigate multi-typed interactive patterns between users and items in recommender systems. KHGT is built upon a graph-structured neural architecture to capture type-specific behavior characteristics. We show that KHGT consistently outperforms many state-of-the-art recommendation methods across various evaluation settings.
  arXiv  Detail & Related papers  (2021-10-08T09:44:00Z)
• Graph Meta Network for Multi-Behavior Recommendation [24.251784947151755]
  We propose a Multi-Behavior recommendation framework with Graph Meta Network to incorporate the multi-behavior pattern modeling into a meta-learning paradigm. Our developed MB-GMN empowers the user-item interaction learning with the capability of uncovering type-dependent behavior representations.
  arXiv  Detail & Related papers  (2021-10-08T08:38:27Z)
• Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation [61.114580368455236]
  User purchasing prediction with multi-behavior information remains a challenging problem for current recommendation systems. We propose the concept of hyper meta-path to construct hyper meta-paths or hyper meta-graphs to explicitly illustrate the dependencies among different behaviors of a user. Thanks to the recent success of graph contrastive learning, we leverage it to learn embeddings of user behavior patterns adaptively instead of assigning a fixed scheme to understand the dependencies among different behaviors.
  arXiv  Detail & Related papers  (2021-09-07T04:28:09Z)
• Modeling High-order Interactions across Multi-interests for Micro-video Reommendation [65.16624625748068]
  We propose a Self-over-Co Attention module to enhance user's interest representation. In particular, we first use co-attention to model correlation patterns across different levels and then use self-attention to model correlation patterns within a specific level.
  arXiv  Detail & Related papers  (2021-04-01T07:20:15Z)
• Disentangled Graph Collaborative Filtering [100.26835145396782]
  Disentangled Graph Collaborative Filtering (DGCF) is a new model for learning informative representations of users and items from interaction data. By modeling a distribution over intents for each user-item interaction, we iteratively refine the intent-aware interaction graphs and representations. DGCF achieves significant improvements over several state-of-the-art models like NGCF, DisenGCN, and MacridVAE.
  arXiv  Detail & Related papers  (2020-07-03T15:37:25Z)

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.