Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network

• URL: http://arxiv.org/abs/2110.04002v1
• Date: Fri, 8 Oct 2021 09:54:43 GMT
• Title: Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network
• Authors: Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Bo Zhang, Liefeng Bo
• Abstract summary: This work proposes a Memory-Augmented Transformer Networks (MATN) to enable the recommendation with multiplex behavioral relational information. In our MATN framework, we first develop a transformer-based multi-behavior relation encoder, to make the learned interaction representations be reflective of the cross-type behavior relations. A memory attention network is proposed to supercharge MATN capturing the contextual signals of different types of behavior into the category-specific latent embedding space.
• Score: 25.563806871858073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Capturing users' precise preferences is of great importance in various recommender systems (eg., e-commerce platforms), which is the basis of how to present personalized interesting product lists to individual users. In spite of significant progress has been made to consider relations between users and items, most of the existing recommendation techniques solely focus on singular type of user-item interactions. However, user-item interactive behavior is often exhibited with multi-type (e.g., page view, add-to-favorite and purchase) and inter-dependent in nature. The overlook of multiplex behavior relations can hardly recognize the multi-modal contextual signals across different types of interactions, which limit the feasibility of current recommendation methods. To tackle the above challenge, this work proposes a Memory-Augmented Transformer Networks (MATN), to enable the recommendation with multiplex behavioral relational information, and joint modeling of type-specific behavioral context and type-wise behavior inter-dependencies, in a fully automatic manner. In our MATN framework, we first develop a transformer-based multi-behavior relation encoder, to make the learned interaction representations be reflective of the cross-type behavior relations. Furthermore, a memory attention network is proposed to supercharge MATN capturing the contextual signals of different types of behavior into the category-specific latent embedding space. Finally, a cross-behavior aggregation component is introduced to promote the comprehensive collaboration across type-aware interaction behavior representations, and discriminate their inherent contributions in assisting recommendations. Extensive experiments on two benchmark datasets and a real-world e-commence user behavior data demonstrate significant improvements obtained by MATN over baselines. Codes are available at: https://github.com/akaxlh/MATN.

Related papers

• 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)
• BiVRec: Bidirectional View-based Multimodal Sequential Recommendation [55.87443627659778]
  We propose an innovative framework, BivRec, that jointly trains the recommendation tasks in both ID and multimodal views. BivRec achieves state-of-the-art performance on five datasets and showcases various practical advantages.
  arXiv  Detail & Related papers  (2024-02-27T09:10:41Z)
• Feature Decoupling-Recycling Network for Fast Interactive Segmentation [79.22497777645806]
  Recent interactive segmentation methods iteratively take source image, user guidance and previously predicted mask as the input. We propose the Feature Decoupling-Recycling Network (FDRN), which decouples the modeling components based on their intrinsic discrepancies.
  arXiv  Detail & Related papers  (2023-08-07T12:26:34Z)
• 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-Behavior Sequential Recommendation with Temporal Graph Transformer [66.10169268762014]
  We tackle the dynamic user-item relation learning with the awareness of multi-behavior interactive patterns. We propose a new Temporal Graph Transformer (TGT) recommendation framework to jointly capture dynamic short-term and long-range user-item interactive patterns.
  arXiv  Detail & Related papers  (2022-06-06T15:42:54Z)
• 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)

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.