# Dual-Phase Playtime-guided Recommendation: Interest Intensity Exploration and Multimodal Random Walks

arXiv: https://arxiv.org/abs/2508.14058 · HTML: https://mlsanity.com/paper/2508.14058/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 9 Aug 2025.

## Builds on (most cited first, 18 of 18)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Bootstrap Latent Representations for Multi-modal Recommendation](https://mlsanity.com/paper/2207.05969/) (2022)
- [DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation](https://mlsanity.com/paper/2211.10486/) (2022)
- [Multi-View Graph Convolutional Network for Multimedia Recommendation](https://mlsanity.com/paper/2308.03588/) (2023)
- [DGCN: Diversified Recommendation with Graph Convolutional Networks](https://mlsanity.com/paper/2108.06952/) (2021)
- [Denoising Diffusion Recommender Model](https://mlsanity.com/paper/2401.06982/) (2024)
- [Post-Training Attribute Unlearning in Recommender Systems](https://mlsanity.com/paper/2403.06737/) (2024)
- [Information Recovery-Driven Deep Incomplete Multiview Clustering Network](https://mlsanity.com/paper/2304.00429/) (2023)
- [Sliding Spectrum Decomposition for Diversified Recommendation](https://mlsanity.com/paper/2107.05204/) (2021)
- [Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network](https://mlsanity.com/paper/2202.03392/) (2022)
- [Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal Recommendation](https://mlsanity.com/paper/2412.14978/) (2024)
- [Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation](https://mlsanity.com/paper/2206.05020/) (2022)
- [Recommending Accurate and Diverse Items Using Bilateral Branch Network](https://mlsanity.com/paper/2101.00781/) (2021)
- [Reconciling the accuracy-diversity trade-off in recommendations](https://mlsanity.com/paper/2307.15142/) (2023)
- [Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation](https://mlsanity.com/paper/2006.03715/) (2020)
- [Multi-Relational Contrastive Learning for Recommendation](https://mlsanity.com/paper/2309.01103/) (2023)
- [Dynamic Multi-Behavior Sequence Modeling for Next Item Recommendation](https://mlsanity.com/paper/2301.12105/) (2023)
- [Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation](https://mlsanity.com/paper/2309.14938/) (2023)

## Built on by (most cited first, 3 of 3)

- [MMGRid: Navigating Temporal-aware and Cross-domain Generative Recommendation via Model Merging](https://mlsanity.com/paper/2601.15930/) (2026)
- [PaperFit: Vision-in-the-Loop Typesetting Optimization for Scientific Documents](https://mlsanity.com/paper/2605.10341/) (2026)
- [Diversity Recommendation via Causal Deconfounding of Co-purchase Relations and Counterfactual Exposure](https://mlsanity.com/paper/2512.17733/) (2025)

## Abstract (arXiv)

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.