Dual-Phase Playtime-guided Recommendation: Interest Intensity Exploration and Multimodal Random Walks
This addresses the need for scalable and engaging recommendation systems in the video game industry, though it appears incremental by building on prior work on accuracy and diversity.
The paper tackles the problem of video game recommendation by jointly optimizing accuracy and diversity, using playtime data and multimodal information. The proposed DP2Rec model outperforms existing methods in both accuracy and diversity on a real-world dataset.
The explosive growth of the video game industry has created an urgent need for recommendation systems that can scale with expanding catalogs and maintain user engagement. While prior work has explored accuracy and diversity in recommendations, existing models underutilize playtime, a rich behavioral signal unique to gaming platforms, and overlook the potential of multimodal information to enhance diversity. In this paper, we propose DP2Rec, a novel Dual-Phase Playtime-guided Recommendation model designed to jointly optimize accuracy and diversity. First, we introduce a playtime-guided interest intensity exploration module that separates strong and weak preferences via dual-beta modeling, enabling fine-grained user profiling and more accurate recommendations. Second, we present a playtime-guided multimodal random walks module that simulates player exploration using transitions guided by both playtime-derived interest similarity and multimodal semantic similarity. This mechanism preserves core preferences while promoting cross-category discovery through latent semantic associations and adaptive category balancing. Extensive experiments on a real-world game dataset show that DP2Rec outperforms existing methods in both recommendation accuracy and diversity.