1 citations · 1 across the 5 of their papers we have counts for
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DREAM: Dynamic Refinement of Early Assignment Mappings
Liwei Guan, Huanjie Wang, Hongwei Zhang +2
Generative recommendation advances item retrieval by reformulating it as autoregressive generation of Semantic IDs (SIDs), compact token sequences that encode item semantics. While…
Break the Inaccessible Boundary: Distilling Post-Conversion Content for User Retention Modeling
Tianbao Ma, Ruochen Yang, Chengen Li +7
User retention is a key metric to measure long-term engagement in modern platforms. In real-time bidding (RTB) advertising system for user re-engagement, the retention model is req…
UniMixer: A Unified Architecture for Scaling Laws in Recommendation Systems
Mingming Ha, Guanchen Wang, Linxun Chen +9
In recent years, the scaling laws of recommendation models have attracted increasing attention, which govern the relationship between performance and parameters/FLOPs of recommende…
PushGen: Push Notifications Generation with LLM
Shifu Bie, Jiangxia Cao, Zixiao Luo +9
We present PushGen, an automated framework for generating high-quality push notifications comparable to human-crafted content. With the rise of generative models, there is growing…
Selection and Exploitation of High-Quality Knowledge from Large Language Models for Recommendation
Guanchen Wang, Mingming Ha, Tianbao Ma +4
In recent years, there has been growing interest in leveraging the impressive generalization capabilities and reasoning ability of large language models (LLMs) to improve the perfo…
Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems
Xiaolin Zheng, Rui Wu, Zhongxuan Han +3
Recommender systems are fundamental information filtering techniques to recommend content or items that meet users' personalities and potential needs. As a crucial solution to addr…