1 citations · 1 across the 7 of their papers we have counts for
12 papers
ALPBench: A Benchmark for Attribution-level Long-term Personal Behavior Understanding
Lu Ren, Junda She, Xinchen Luo +23
Recent advances in large language models have highlighted their potential for personalized recommendation, where accurately capturing user preferences remains a key challenge. Leve…
OneMall: One Architecture, More Scenarios -- End-to-End Generative Recommender Family at Kuaishou E-Commerce
Kun Zhang, Jingming Zhang, Wei Cheng +29
In the wave of generative recommendation, we present OneMall, an end-to-end generative recommendation framework tailored for e-commerce services at Kuaishou. Our OneMall systematic…
Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers
Zhiyang Zhang, Junda She, Kuo Cai +8
Leveraging the vast open-world knowledge and understanding capabilities of Large Language Models (LLMs) to develop general-purpose, semantically-aware recommender systems has emerg…
PROMISE: Process Reward Models Unlock Test-Time Scaling Laws in Generative Recommendations
Chengcheng Guo, Kuo Cai, Yu Zhou +5
Generative Recommendation has emerged as a promising paradigm, reformulating recommendation as a sequence-to-sequence generation task over hierarchical Semantic IDs. However, exist…
OpenOneRec Technical Report
Guorui Zhou, Honghui Bao, Jiaming Huang +44
While the OneRec series has successfully unified the fragmented recommendation pipeline into an end-to-end generative framework, a significant gap remains between recommendation sy…
OneRec-Think: In-Text Reasoning for Generative Recommendation
Zhanyu Liu, Shiyao Wang, Xingmei Wang +23
The powerful generative capacity of Large Language Models (LLMs) has instigated a paradigm shift in recommendation. However, existing generative models (e.g., OneRec) operate as im…