Category-based and Popularity-guided Video Game Recommendation: A Balance-oriented Framework

2026-04-16

AI Summary

This work addresses the limitations of existing game recommendation systems, which prioritize accuracy at the expense of diversity and struggle to effectively integrate category and popularity information. To overcome these challenges, the authors propose CPGRec, a novel framework that jointly models game categories and popularity for the first time. CPGRec employs a three-module collaborative mechanism—comprising accuracy-driven, diversity-driven, and integrated modules—to enhance the representation of long-tail games by leveraging multi-category neighbors and high-popularity nodes within the graph structure. Additionally, a negative sampling score reweighting strategy is introduced to balance the dual objectives of accuracy and diversity. Experimental results on the Steam dataset demonstrate that the proposed method significantly improves both recommendation accuracy and diversity.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Game Theory and Economic Paradigms: Cooperative Game Theory

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

In recent years, the video game industry has experienced substantial growth, presenting players with a vast array of game choices. This surge in options has spurred the need for a specialized recommender system tailored for video games. However, current video game recommendation approaches tend to prioritize accuracy over diversity, potentially leading to unvaried game suggestions. In addition, the existing game recommendation methods commonly lack the ability to establish strict connections between games to enhance accuracy. Furthermore, many existing diversity-focused methods fail to leverage crucial item information, such as item category and popularity during neighbor modeling and message propagation. To address these challenges, we introduce a novel framework, called CPGRec, comprising three modules, namely accuracy-driven, diversity-driven, and comprehensive modules. The first module extends the state-of-the-art accuracy-focused game recommendation method by connecting games in a more stringent manner to enhance recommendation accuracy. The second module connects neighbors with diverse categories within the proposed game graph and harnesses the advantages of popular game nodes to amplify the influence of long-tail games within the player-game bipartite graph, thereby enriching recommendation diversity. The third module combines the above two modules and employs a new negative-sample rating score reweighting method to balance accuracy and diversity. Experimental results on the Steam dataset demonstrate the effectiveness of our proposed method in improving game recommendations. The dataset and source codes are anonymously released at: https://github.com/CPGRec2024/CPGRec.git.

Problem

• video game recommendation
• accuracy-diversity trade-off
• category information
• popularity bias
• neighbor modeling

Innovation

• video game recommendation
• category-aware modeling
• popularity-guided propagation
• accuracy-diversity balance
• graph-based recommender system