376 citations · 598 across the 19 of their papers we have counts for
5 papers · 1 filter
Learning When to Think: Utility-Guided Adaptive Reasoning for Efficient Text Retrieval
Yingtao Luo, Kangyi Zhao, Shiyi Du +1
Reasoning-conditioned embeddings can improve retrieval by progressively refining semantic constraints. However, additional reasoning may introduce over-generalized or semantically…
Improving Multi-Interest Network with Stable Learning
Zhaocheng Liu, Yingtao Luo, Di Zeng +4
Modeling users' dynamic preferences from historical behaviors lies at the core of modern recommender systems. Due to the diverse nature of user interests, recent advances propose t…
AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation
Juyong Jiang, Peiyan Zhang, Yingtao Luo +6
Sequential recommendation (SR) aims to model users dynamic preferences from a series of interactions. A pivotal challenge in user modeling for SR lies in the inherent variability o…
Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation with Pre-training
Juyong Jiang, Peiyan Zhang, Yingtao Luo +6
Sequential recommendation systems are integral to discerning temporal user preferences. Yet, the task of learning from abbreviated user interaction sequences poses a notable challe…
STAN: Spatio-Temporal Attention Network for Next Location Recommendation
Yingtao Luo, Qiang Liu, Zhaocheng Liu
The next location recommendation is at the core of various location-based applications. Current state-of-the-art models have attempted to solve spatial sparsity with hierarchical g…