855 citations · 2.6k across the 59 of their papers we have counts for
5 papers · 1 filter
SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval
Liang Pang, Jun Xu, Qingyao Ai +3
In learning-to-rank for information retrieval, a ranking model is automatically learned from the data and then utilized to rank the sets of retrieved documents. Therefore, an ideal…
Explainable Product Search with a Dynamic Relation Embedding Model
Qingyao Ai, Yongfeng Zhang, Keping Bi +1
Product search is one of the most popular methods for customers to discover products online. Most existing studies on product search focus on developing effective retrieval models…
Conversational Product Search Based on Negative Feedback
Keping Bi, Qingyao Ai, Yongfeng Zhang +1
Intelligent assistants change the way people interact with computers and make it possible for people to search for products through conversations when they have purchase needs. Dur…
A Zero Attention Model for Personalized Product Search
Qingyao Ai, Daniel N. Hill, S. V. N. Vishwanathan +1
Product search is one of the most popular methods for people to discover and purchase products on e-commerce websites. Because personal preferences often have an important influenc…
A Deep Look into Neural Ranking Models for Information Retrieval
Jiafeng Guo, Yixing Fan, Liang Pang +6
Ranking models lie at the heart of research on information retrieval (IR). During the past decades, different techniques have been proposed for constructing ranking models, from tr…