13 papers
The User Asks, Platforms Compete: How Agentic Recommendation Markets Take Shape
Deyao Hong, Kehan Zheng, Qian Li +3
Online recommendation has traditionally taken place after a user enters a platform, which determines the candidate pool and the ranking shown to the user. LLM-based user agents ena…
Beyond Positive Signals: Unlocking Implicit Negative Behaviors for Enhanced Sequential User Modeling
Zexuan Cheng, Yue Liu, Jun Zhang +1
User behavior sequence modeling has become a central component in modern click-through rate (CTR) prediction. Over the past years, the community has invested substantial effort int…
PRISM: Parallel Residual Iterative Sequence Model
Jie Jiang, Ke Cheng, Xin Xu +8
Generative sequence modeling faces a fundamental tension between the expressivity of Transformers and the efficiency of linear sequence models. Existing efficient architectures are…
End-to-End Semantic ID Generation for Generative Advertisement Recommendation
Jie Jiang, Xinxun Zhang, Enming Zhang +8
Generative Recommendation (GR) has excelled by framing recommendation as next-token prediction. This paradigm relies on Semantic IDs (SIDs) to tokenize large-scale items into discr…
S-GRec: Personalized Semantic-Aware Generative Recommendation with Asymmetric Advantage
Jie Jiang, Hongbo Tang, Wenjie Wu +6
Generative recommendation models sequence generation to produce items end-to-end, but training from behavioral logs often provides weak supervision on underlying user intent. Altho…
SCoTER: Structured Chain-of-Thought Transfer for Enhanced Recommendation
Jie Jiang, Yang Wu, Qian Li +7
Harnessing the reasoning power of Large Language Models (LLMs) for recommender systems is hindered by two fundamental challenges. First, current approaches lack a mechanism for aut…