CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations
cs.IR, cs.AI
Submitted: 2026-04-16
Updated: 2026-08-27
Comments: Published in ACM Transactions on Information Systems (TOIS). 43 pages, 9 figures
Journal ref: ACM Trans. Inf. Syst. 44, 3, Article 66 (March 2026), 44 pages
DOI: 10.1145/378926
Code: https://github.com/HsipingLi/CPGRec-Plus
License: http://creativecommons.org/licenses/by/4.0/
The gist: The rapid expansion of gaming industry requires advanced recommender systems tailored to its dynamic landscape.
Terminology
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 two Steam datasets demonstrate CPGRec+'s superior accuracy and diversity over state-of-the-art models. The code is accessible at https://github.com/HsipingLi/CPGRec-Plus.
Sources
- M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
- Inductive Representation Learning on Large Graphs
- Semi-Supervised Classification with Graph Convolutional Networks
- Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions
- BPR: Bayesian Personalized Ranking from Implicit Feedback
- Recommendations by Concise User Profiles from Review Text
- Incomplete In-context Learning
- Qwen2.5 Technical Report
- ResilPhase: Plug-and-Play Phase Mapping and Noise-Resilient Macro-Trajectory Extrapolation for Diffusion Acceleration
- Seeing the End at Step Zero: Accelerating Diffusion MLLMs via MLP Sparsity-Aware Truncation
- Robust Uncertainty Quantification for Self-Evolving Large Language Models via Continual Domain Pretraining
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