Towards Multi-Behavior Multi-Task Recommendation via Behavior-informed Graph Embedding Learning

• URL: http://arxiv.org/abs/2601.07294v1
• Date: Mon, 12 Jan 2026 08:04:08 GMT
• Title: Towards Multi-Behavior Multi-Task Recommendation via Behavior-informed Graph Embedding Learning
• Authors: Wenhao Lai, Weike Pan, Zhong Ming,
• Abstract summary: Multi-behavior recommendation (MBR) aims to improve the performance of the target behavior (i.e., purchase) by leveraging auxiliary behaviors (e.g., click, favourite)<n>In real-world scenarios, a recommendation method often needs to process different types of behaviors and generate personalized lists for each task (i.e., each behavior type)
• Score: 13.503641677199857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-behavior recommendation (MBR) aims to improve the performance w.r.t. the target behavior (i.e., purchase) by leveraging auxiliary behaviors (e.g., click, favourite). However, in real-world scenarios, a recommendation method often needs to process different types of behaviors and generate personalized lists for each task (i.e., each behavior type). Such a new recommendation problem is referred to as multi-behavior multi-task recommendation (MMR). So far, the most powerful MBR methods usually model multi-behavior interactions using a cascading graph paradigm. Although significant progress has been made in optimizing the performance of the target behavior, it often neglects the performance of auxiliary behaviors. To compensate for the deficiencies of the cascading paradigm, we propose a novel solution for MMR, i.e., behavior-informed graph embedding learning (BiGEL). Specifically, we first obtain a set of behavior-aware embeddings by using a cascading graph paradigm. Subsequently, we introduce three key modules to improve the performance of the model. The cascading gated feedback (CGF) module enables a feedback-driven optimization process by integrating feedback from the target behavior to refine the auxiliary behaviors preferences. The global context enhancement (GCE) module integrates the global context to maintain the user's overall preferences, preventing the loss of key preferences due to individual behavior graph modeling. Finally, the contrastive preference alignment (CPA) module addresses the potential changes in user preferences during the cascading process by aligning the preferences of the target behaviors with the global preferences through contrastive learning. Extensive experiments on two real-world datasets demonstrate the effectiveness of our BiGEL compared with ten very competitive methods.

Related papers

• From Agnostic to Specific: Latent Preference Diffusion for Multi-Behavior Sequential Recommendation [28.437926520491445]
  Multi-behavior sequential recommendation (MBSR) aims to learn the dynamic and heterogeneous interactions of users' multi-behavior sequences.<n>Recent concerns are shifting from behavior-fixed to behavior-specific recommendation.<n>We propose textbfFatsMB, a framework based diffusion model that guides preference generation.
  arXiv  Detail & Related papers  (2026-02-26T15:48:09Z)
• Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation [31.495904374599533]
  We propose a novel model-agnostic Hierarchical Graph Information Bottleneck (HGIB) framework for multi-behavior recommendation.<n>Our framework optimize the learning of compact yet sufficient representations that preserve essential information for target behavior prediction.<n>We conduct comprehensive experiments on three real-world public datasets, which demonstrate the superior effectiveness of our framework.
  arXiv  Detail & Related papers  (2025-07-21T08:53:49Z)
• Large Language Model Empowered Recommendation Meets All-domain Continual Pre-Training [60.38082979765664]
  CPRec is an All-domain Continual Pre-Training framework for Recommendation.<n>It holistically align LLMs with universal user behaviors through the continual pre-training paradigm.<n>We conduct experiments on five real-world datasets from two distinct platforms.
  arXiv  Detail & Related papers  (2025-04-11T20:01:25Z)
• Combinatorial Optimization Perspective based Framework for Multi-behavior Recommendation [23.26102452699347]
  We propose a novel multi-behavior recommendation framework based on the optimization perspective, named COPF.<n>In the prediction step, we improve both forward and backward propagation during the generation and aggregation of multiple experts.<n>Experiments on three real-world datasets indicate the superiority of COPF.
  arXiv  Detail & Related papers  (2025-02-04T11:19:47Z)
• HEC-GCN: Hypergraph Enhanced Cascading Graph Convolution Network for Multi-Behavior Recommendation [41.65320959602054]
  We propose a novel approach named Hypergraph Enhanced Cascading Graph Convolution Network for multi-behavior recommendation (HEC-GCN)<n>To be specific, we first explore both fine- and coarse-grained correlations among users or items of each behavior by simultaneously modeling the behavior-specific interaction graph and its corresponding hypergraph in a cascaded manner.
  arXiv  Detail & Related papers  (2024-12-19T02:57:02Z)
• 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)
• APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation [86.29366168836141]
  We propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR) APGL4SR incorporates adaptive and personalized global collaborative information into sequential recommendation systems. As a generic framework, APGL4SR can outperform other baselines with significant margins.
  arXiv  Detail & Related papers  (2023-11-06T01:33:24Z)
• Multi-behavior Self-supervised Learning for Recommendation [36.42241501002167]
  We propose a Multi-Behavior Self-Supervised Learning (MBSSL) framework together with an adaptive optimization method. Specifically, we devise a behavior-aware graph neural network incorporating the self-attention mechanism to capture behavior multiplicity and dependencies. Experiments on five real-world datasets demonstrate the consistent improvements obtained by MBSSL over ten state-of-the art (SOTA) baselines.
  arXiv  Detail & Related papers  (2023-05-22T15:57:32Z)
• 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)
• 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)
• 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.