27 citations · 62 across the 10 of their papers we have counts for
10 papers
Multi-factor Sequential Re-ranking with Perception-Aware Diversification
Yue Xu, Hao Chen, Zefan Wang +8
Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications. In feed produc…
Multi-channel Integrated Recommendation with Exposure Constraints
Yue Xu, Qijie Shen, Jianwen Yin +6
Integrated recommendation, which aims at jointly recommending heterogeneous items from different channels in a main feed, has been widely applied to various online platforms. Thoug…
Entire Space Learning Framework: Unbias Conversion Rate Prediction in Full Stages of Recommender System
Shanshan Lyu, Qiwei Chen, Tao Zhuang +1
Recommender system is an essential part of online services, especially for e-commerce platform. Conversion Rate (CVR) prediction in RS plays a significant role in optimizing Gross…
MAKE: Vision-Language Pre-training based Product Retrieval in Taobao Search
Xiaoyang Zheng, Zilong Wang, Ke Xu +4
Taobao Search consists of two phases: the retrieval phase and the ranking phase. Given a user query, the retrieval phase returns a subset of candidate products for the following ra…
Hierarchical Multi-Interest Co-Network For Coarse-Grained Ranking
Xu Yuan, Chen Xu, Qiwei Chen +3
In this era of information explosion, a personalized recommendation system is convenient for users to get information they are interested in. To deal with billions of users and ite…
Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction
Qiwei Chen, Yue Xu, Changhua Pei +3
Recent studies on Click-Through Rate (CTR) prediction has reached new levels by modeling longer user behavior sequences. Among others, the two-stage methods stand out as the state-…
Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
Zhifang Fan, Dan Ou, Yulong Gu +7
Modeling user's historical feedback is essential for Click-Through Rate Prediction in personalized search and recommendation. Existing methods usually only model users' positive fe…
IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search
Dian Cheng, Jiawei Chen, Wenjun Peng +5
A good personalized product search (PPS) system should not only focus on retrieving relevant products, but also consider user personalized preference. Recent work on PPS mainly ado…
Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling
Jia-Qi Yang, Xiang Li, Shuguang Han +4
Conversion rate (CVR) prediction is one of the most critical tasks for digital display advertising. Commercial systems often require to update models in an online learning manner t…
Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning
Ryuichi Takanobu, Tao Zhuang, Minlie Huang +3
In this paper, we investigate the task of aggregating search results from heterogeneous sources in an E-commerce environment. First, unlike traditional aggregated web search that m…