Rank models play a key role in industrial recommender systems, advertising, and search engines. Existing works utilize semantic tags and user-item interaction behaviors, e.g., clicks, views, etc., to predict the user interest and the item hidden representation for estimating the user-item preference score. However, these behavior-tag-based models encounter great challenges and reduced effectiveness when user-item interaction activities are insufficient, which we called "the long-tail ranking problem". Existing rank models ignore this problem, but its common and important because any user or item can be long-tailed once they are not consistently active for a short period. In this paper, we propose a novel neighbor enhancement structure to help train the representation of the target user or item. It takes advantage of similar neighbors (static or dynamic similarity) with multi-level attention operations balancing the weights of different neighbors. Experiments on the well-known public dataset MovieLens 1M demonstrate the efficiency of the method over the baseline behavior-tag-based model with an absolute CTR AUC gain of 0.0259 on the long-tail user dataset.
Pretraining De-Biased Language Model with Large-scale Click Logs for Document Ranking
预训练语言模型在各种大规模信息检索任务中取得了巨大的成功。然而，大多数预训练任务是基于伪造的检索数据，其中定制规则产生的查询被假设为用户对给定文档或段落发出的查询。因此，我们探索使用大规模点击日志来预训练语言模型，而不是对模拟查询进行回复。具体来说，我们建议使用用户行为特征来预训练一个用于文档排序的去偏见语言模型。对百度脱敏点击日志的大量实验验证了我们方法的有效性。我们的团队在WSDM Cup 2023网络搜索预训练中以12.16525的折扣累积增益@10（DCG@10）得分在最终排行榜上获得第一名。
GARDRec: Decision-Level Graph Grounding for Large Language Model Recommendation
Harbin Institute of Technology Qingdao Research Institute（哈尔滨工业大学（青岛）研究院）；Harbin Institute of Technology (Weihai)（哈尔滨工业大学（威海））；Shandong Provincial Key Laboratory of Digital Service Computing Technology and Systems（山东省数字服务计算技术系统重点实验室）
A Brand-level Ranking System with the Customized Attention-GRU Model
Alibaba Group（阿里巴巴集团）；Zhejiang University（浙江大学）；Northwest University of China（中国西北大学）；Alibaba-Zhejiang University Joint Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）
