24 citations · 87 across the 13 of their papers we have counts for
6 papers · 1 filter
Improving Text Matching in E-Commerce Search with A Rationalizable, Intervenable and Fast Entity-Based Relevance Model
Jiong Cai, Yong Jiang, Yue Zhang +10
Discovering the intended items of user queries from a massive repository of items is one of the main goals of an e-commerce search system. Relevance prediction is essential to the…
Path-based Deep Network for Candidate Item Matching in Recommenders
Houyi Li, Zhihong Chen, Chenliang Li +5
The large-scale recommender system mainly consists of two stages: matching and ranking. The matching stage (also known as the retrieval step) identifies a small fraction of relevan…
A Hybrid Bandit Framework for Diversified Recommendation
Qinxu Ding, Yong Liu, Chunyan Miao +2
The interactive recommender systems involve users in the recommendation procedure by receiving timely user feedback to update the recommendation policy. Therefore, they are widely…
Pre-training Graph Transformer with Multimodal Side Information for Recommendation
Yong Liu, Susen Yang, Chenyi Lei +5
Side information of items, e.g., images and text description, has shown to be effective in contributing to accurate recommendations. Inspired by the recent success of pre-training…
Recent Advances in Diversified Recommendation
Qiong Wu, Yong Liu, Chunyan Miao +3
With the rapid development of recommender systems, accuracy is no longer the only golden criterion for evaluating whether the recommendation results are satisfying or not. In recen…
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…