31 citations · 46 across the 4 of their papers we have counts for
5 papers
Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation
Jiang Zhang, Sumit Kumar, Wei Chang +7
The task of item-to-item (I2I) retrieval is to identify a set of relevant and highly engaging items based on a given trigger item. It is a crucial component in modern recommendatio…
Order-agnostic Identifier for Large Language Model-based Generative Recommendation
Xinyu Lin, Haihan Shi, Wenjie Wang +4
Leveraging Large Language Models (LLMs) for generative recommendation has attracted significant research interest, where item tokenization is a critical step. It involves assigning…
Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks
Zhiyuan Cheng, Cheng Han, James Liang +3
Monocular Depth Estimation (MDE) plays a vital role in applications such as autonomous driving. However, various attacks target MDE models, with physical attacks posing significant…
User Welfare Optimization in Recommender Systems with Competing Content Creators
Fan Yao, Yiming Liao, Mingzhe Wu +6
Driven by the new economic opportunities created by the creator economy, an increasing number of content creators rely on and compete for revenue generated from online content reco…
Large Language Models are Learnable Planners for Long-Term Recommendation
Wentao Shi, Xiangnan He, Yang Zhang +5
Planning for both immediate and long-term benefits becomes increasingly important in recommendation. Existing methods apply Reinforcement Learning (RL) to learn planning capacity b…