CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations

[2604.14586] CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations Skip to main content Search Submit Donate Log in Computer Science > Information Retrieval arXiv:2604.14586 (cs) [Submitted on 16 Apr 2026 ( v1 ), last revised 27 Aug 2026 (this version, v4)] Title: CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations Authors: Xiping Li , Aier Yang , Jianghong Ma , Kangzhe Liu , Shanshan Feng , Haijun Zhang , Yi Zhao View PDF HTML (experimental) Abstract: The rapid expansion of gaming industry requires advanced recommender systems tailored to its dynamic landscape. Existing Graph Neural Network (GNN)-based methods primarily prioritize accuracy over diversity, overlooking their inherent trade-off. To address this, we previously proposed CPGRec, a balance-oriented gaming recommender system. However, CPGRec fails to account for critical disparities in player-game interactions, which carry varying significance in reflecting players' personal preferences and may exacerbate over-smoothness issues inherent in GNN-based models. Moreover, existing approaches underutilize the reasoning capabilities and extensive knowledge of large language models (LLMs) in addressing these limitations. To bridge this gap, we propose two new modules. First, Preference-informed Edge Reweighting (PER) module assigns signed edge weights to qualitatively distinguish significant player interests and disinterests while then quantitatively measuring preference strength to mitigate over-smoothing in graph convolutions. Second, Preference-informed Representation Generation (PRG) module leverages LLMs to generate contextualized descriptions of games and players by reasoning personal preferences from comparing global and personal interests, thereby refining representations of players and games. Experiments on \textcolor{black}{two Steam datasets} demonstrate CPGRec+'s superior accuracy and diversity over state-of-the-art models. The code is accessible at this https URL . Comments: Published in ACM Transactions on Information Systems (TOIS). 43 pages, 9 figures Subjects: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2604.14586 [cs.IR] (or arXiv:2604.14586v4 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2604.14586 Focus to learn more arXiv-issued DOI via DataCite Journal reference: ACM Trans. Inf. Syst. 44, 3, Article 66 (March 2026), 44 pages Related DOI : https://doi.org/10.1145/378926 Focus to learn more DOI(s) linking to related resources Submission history From: Xiping Li [ view email ] [v1] Thu, 16 Apr 2026 03:25:18 UTC (6,130 KB) [v2] Sun, 19 Apr 2026 01:21:34 UTC (6,131 KB) [v3] Tue, 21 Jul 2026 17:16:37 UTC (5,422 KB) [v4] Thu, 27 Aug 2026 10:13:06 UTC (5,422 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 cs.AI 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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