27 citations · 62 across the 10 of their papers we have counts for
9 papers · 1 filter
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…
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…