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

[2604.14598] Category-based and Popularity-guided Video Game Recommendation: A Balance-oriented Framework Skip to main content Search Submit Donate Log in Computer Science > Information Retrieval arXiv:2604.14598 (cs) [Submitted on 16 Apr 2026 ( v1 ), last revised 27 Aug 2026 (this version, v4)] Title: Category-based and Popularity-guided Video Game Recommendation: A Balance-oriented Framework Authors: Xiping Li , Jianghong Ma , Kangzhe Liu , Shanshan Feng , Haijun Zhang , Yutong Wang View PDF HTML (experimental) 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: this https URL . Comments: Published in The Web Conference (WWW) 2024. 11 pages, 8 figures Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2604.14598 [cs.IR] (or arXiv:2604.14598v4 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2604.14598 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1145/3589334.3645573 Focus to learn more DOI(s) linking to related resources Submission history From: Xiping Li [ view email ] [v1] Thu, 16 Apr 2026 04:03:45 UTC (2,764 KB) [v2] Sun, 19 Apr 2026 01:18:00 UTC (2,762 KB) [v3] Tue, 21 Jul 2026 17:16:48 UTC (2,507 KB) [v4] Thu, 27 Aug 2026 10:11:03 UTC (2,507 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.IR < prev | next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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