Enhanced Influence-aware Group Recommendation for Online Media Propagation

• URL: http://arxiv.org/abs/2507.01616v1
• Date: Wed, 02 Jul 2025 11:34:17 GMT
• Title: Enhanced Influence-aware Group Recommendation for Online Media Propagation
• Authors: Chengkun He, Xiangmin Zhou, Chen Wang, Longbing Cao, Jie Shao, Xiaodong Li, Guang Xu, Carrie Jinqiu Hu, Zahir Tari,
• Abstract summary: Group recommendation (GR) aims to provide more accurate and engaging content to a set of users rather than individuals.<n>Recently, influence-aware GR has emerged as a promising direction, as it considers the impact of social influence on group decision-making.<n>We propose an Enhanced Influence-aware Group Recommendation (EIGR) framework to tackle these issues.
• Score: 34.042793306362114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Group recommendation over social media streams has attracted significant attention due to its wide applications in domains such as e-commerce, entertainment, and online news broadcasting. By leveraging social connections and group behaviours, group recommendation (GR) aims to provide more accurate and engaging content to a set of users rather than individuals. Recently, influence-aware GR has emerged as a promising direction, as it considers the impact of social influence on group decision-making. In earlier work, we proposed Influence-aware Group Recommendation (IGR) to solve this task. However, this task remains challenging due to three key factors: the large and ever-growing scale of social graphs, the inherently dynamic nature of influence propagation within user groups, and the high computational overhead of real-time group-item matching. To tackle these issues, we propose an Enhanced Influence-aware Group Recommendation (EIGR) framework. First, we introduce a Graph Extraction-based Sampling (GES) strategy to minimise redundancy across multiple temporal social graphs and effectively capture the evolving dynamics of both groups and items. Second, we design a novel DYnamic Independent Cascade (DYIC) model to predict how influence propagates over time across social items and user groups. Finally, we develop a two-level hash-based User Group Index (UG-Index) to efficiently organise user groups and enable real-time recommendation generation. Extensive experiments on real-world datasets demonstrate that our proposed framework, EIGR, consistently outperforms state-of-the-art baselines in both effectiveness and efficiency.

Related papers

• On the Effect of Negative Gradient in Group Relative Deep Reinforcement Optimization [52.76330545825083]
  Reinforcement learning (RL) has become popular in enhancing the reasoning capabilities of large language models (LLMs)<n>We identify a previously unrecognized phenomenon we term Lazy Likelihood Displacement (LLD), wherein the likelihood of correct responses marginally increases or even decreases during training.<n>We develop a method called NTHR, which downweights penalties on tokens contributing to the LLD. Unlike prior DPO-based approaches, NTHR takes advantage of GRPO's group-based structure, using correct responses as anchors to identify influential tokens.
  arXiv  Detail & Related papers  (2025-05-24T18:58:51Z)
• Group-Level Data Selection for Efficient Pretraining [49.18903821780051]
  Group-MATES is an efficient group-level data selection approach to optimize the speed-quality frontier of language model pretraining.<n>Group-MATES parameterizes costly group-level selection with a relational data influence model.
  arXiv  Detail & Related papers  (2025-02-20T16:34:46Z)
• Disentangled Modeling of Preferences and Social Influence for Group Recommendation [16.953172230355268]
  Group recommendation (GR) aims to suggest items for a group of users in social networks.<n>Existing methods either neglect the social influence of individual members or bundle preferences and social influence together.<n>We propose a novel model based on Disentangled Modeling of Preferences and Social Influence for Group Recommendation (DisRec)
  arXiv  Detail & Related papers  (2025-01-20T09:03:18Z)
• 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)
• Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph [68.08590487960475]
  We propose a novel GNN-based framework named Contextualized Factorized Attention for Group identification (CFAG) We devise tripartite graph convolution layers to aggregate information from different types of neighborhoods among users, groups, and items. To cope with the data sparsity issue, we devise a novel propagation augmentation layer, which is based on our proposed factorized attention mechanism.
  arXiv  Detail & Related papers  (2022-11-02T01:42:20Z)
• Focus on the Common Good: Group Distributional Robustness Follows [47.62596240492509]
  This paper proposes a new and simple algorithm that explicitly encourages learning of features that are shared across various groups. While Group-DRO focuses on groups with worst regularized loss, focusing instead, on groups that enable better performance even on other groups, could lead to learning of shared/common features.
  arXiv  Detail & Related papers  (2021-10-06T09:47:41Z)
• Graph Neural Netwrok with Interaction Pattern for Group Recommendation [1.066048003460524]
  We propose the model GIP4GR (Graph Neural Network with Interaction Pattern For Group Recommendation) Specifically, our model use the graph neural network framework with powerful representation capabilities to represent the interaction between group-user-items in the topological structure of the graph. We conducted a lot of experiments on two real-world datasets to illustrate the superior performance of our model.
  arXiv  Detail & Related papers  (2021-09-21T13:42:46Z)
• Overcoming Data Sparsity in Group Recommendation [52.00998276970403]
  Group recommender systems should be able to accurately learn not only users' personal preferences but also preference aggregation strategy. In this paper, we take Bipartite Graphding Model (BGEM), the self-attention mechanism and Graph Convolutional Networks (GCNs) as basic building blocks to learn group and user representations in a unified way.
  arXiv  Detail & Related papers  (2020-10-02T07:11:19Z)
• GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation [24.677145454396822]
  We study the problem of making item recommendations to ephemeral groups, which comprise users with limited or no historical activities together. Existing studies target persistent groups with substantial activity history, while ephemeral groups lack historical interactions. We propose data-driven regularization strategies to exploit both the preference covariance amongst users who are in the same group, as well as the contextual relevance of users' individual preferences to each group.
  arXiv  Detail & Related papers  (2020-06-05T23:18:19Z)

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.