Enhanced Influence-aware Group Recommendation for Online Media Propagation

2025-07-02

AI Summary

This paper addresses three key challenges in group recommendation for social media streams: (1) the large and dynamically growing social graph, (2) time-varying influence propagation within groups, and (3) high computational cost of real-time group–item matching. To tackle these, we propose an efficient dynamic group recommendation framework. Methodologically, we design a graph extraction and sampling strategy to compress the large-scale social graph; formulate a dynamic independent cascade model to capture the temporal evolution of influence propagation; and introduce a two-level hashing-based group index to accelerate real-time matching. Our approach innovatively integrates temporal graph neural networks with differentiable cascade modeling for end-to-end optimization. Extensive experiments on multiple real-world datasets demonstrate that the framework significantly outperforms state-of-the-art methods—achieving a 12.3% improvement in NDCG@10 and a 58% reduction in response latency.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web
• Social Networks and Social Media: Social mining and social search on the Web

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.

Problem

• Enhancing group recommendation by addressing large-scale social graphs
• Modeling dynamic influence propagation in user groups
• Reducing computational overhead for real-time recommendations

Innovation

• Graph Extraction-based Sampling for dynamic social graphs
• Dynamic Independent Cascade model for influence prediction
• Two-level hash-based User Group Index for efficiency