Strategy-aware Bundle Recommender System
Yinwei Wei, Xiaohao Liu, Yunshan Ma, Xiang Wang, Liqiang Nie, Tat-Seng Chua
摘要
A bundle is a group of items that provides improved services to users and increased profits for sellers. However, locating the desired bundles that match the users' tastes still challenges us, due to the sparsity issue. Despite the remarkable performance of existing approaches, we argue that they seldom consider the bundling strategy (i.e., how the items within a bundle are associated with each other) in the bundle recommendation, resulting in the suboptimal user and bundle representations for their interaction prediction. Therefore, we propose to model the strategy-aware user and bundle representations for the bundle recommendation.
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引用它的顶会 Paper7
- Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum HeatingHyunsik Jeon, Jong-eun Lee, Jeongin Yun, U KangWWW 2024 · 被引用 21 次
- Adaptive In-Context Learning with Large Language Models for Bundle GenerationZhu Sun, Kaidong Feng, Jie Yang, Xinghua Qu 等SIGIR 2024 · 被引用 9 次
- Disentangled Contrastive Bundle Recommendation with Conditional DiffusionJiuqiang LiAAAI 2025 · 被引用 5 次
- Fine-tuning Multimodal Large Language Models for Product BundlingXiaohao Liu, Jie Wu, Zhulin Tao, Yunshan Ma 等KDD 2025 · 被引用 3 次
- Modeling Item-Level Dynamic Variability with Residual Diffusion for Bundle RecommendationDong Zhang, Lin Li, Ming Li, Amran Bhuiyan 等AAAI 2026 · 被引用 2 次
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