542 citations · 977 across the 30 of their papers we have counts for
40 papers
A Bird's-eye View of Reranking: from List Level to Page Level
Yunjia Xi, Jianghao Lin, Weiwen Liu +5
Reranking, as the final stage of multi-stage recommender systems, refines the initial lists to maximize the total utility. With the development of multimedia and user interface des…
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation
Haolun Wu, Yingxue Zhang, Chen Ma +4
To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations. Many SOTA methods f…
IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System
Xiangyang Li, Bo Chen, HuiFeng Guo +10
Scoring a large number of candidates precisely in several milliseconds is vital for industrial pre-ranking systems. Existing pre-ranking systems primarily adopt the \textbf{two-tow…
OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
Fuyuan Lyu, Xing Tang, Hong Zhu +4
Learning embedding table plays a fundamental role in Click-through rate(CTR) prediction from the view of the model performance and memory usage. The embedding table is a two-dimens…
A Brief History of Recommender Systems
Zhenhua Dong, Zhe Wang, Jun Xu +2
Soon after the invention of the Internet, the recommender system emerged and related technologies have been extensively studied and applied by both academia and industry. Currently…
Cross Pairwise Ranking for Unbiased Item Recommendation
Qi Wan, Xiangnan He, Xiang Wang +3
Most recommender systems optimize the model on observed interaction data, which is affected by the previous exposure mechanism and exhibits many biases like popularity bias. The lo…