4 papers
Breaking the Likelihood Trap: Consistent Generative Recommendation with Graph-structured Model
Qiya Yang, Xiaoxi Liang, Zeping Xiao +5
Reranking, as the final stage of recommender systems, plays a crucial role in determining the final exposure, directly influencing user experience. Recently, generative reranking h…
DualGR: Generative Retrieval with Long and Short-Term Interests Modeling
Zhongchao Yi, Kai Feng, Xiaojian Ma +5
In large-scale industrial recommendation systems, retrieval must produce high-quality candidates from massive corpora under strict latency. Recently, Generative Retrieval (GR) has…
UMRE: A Unified Monotonic Transformation for Ranking Ensemble in Recommender Systems
Zhengrui Xu, Zhe Yang, Zhengxiao Guo +5
Industrial recommender systems commonly rely on ensemble sorting (ES) to combine predictions from multiple behavioral objectives. Traditionally, this process depends on manually de…
Comment Staytime Prediction with LLM-enhanced Comment Understanding
Changshuo Zhang, Zihan Lin, Shukai Liu +2
In modern online streaming platforms, the comments section plays a critical role in enhancing the overall user experience. Understanding user behavior within the comments section i…