49 citations · 49 across the 3 of their papers we have counts for
8 papers
A Transformer-based Embedding Model for Personalized Product Search
Keping Bi, Qingyao Ai, W. Bruce Croft
Product search is an important way for people to browse and purchase items on E-commerce platforms. While customers tend to make choices based on their personal tastes and preferen…
Artemis: A Novel Annotation Methodology for Indicative Single Document Summarization
Rahul Jha, Keping Bi, Yang Li +4
We describe Artemis (Annotation methodology for Rich, Tractable, Extractive, Multi-domain, Indicative Summarization), a novel hierarchical annotation process that produces indicati…
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 Study of Context Dependencies in Multi-page Product Search
Keping Bi, Choon Hui Teo, Yesh Dattatreya +2
In product search, users tend to browse results on multiple search result pages (SERPs) (e.g., for queries on clothing and shoes) before deciding which item to purchase. Users' cli…
Leverage Implicit Feedback for Context-aware Product Search
Keping Bi, Choon Hui Teo, Yesh Dattatreya +2
Product search serves as an important entry point for online shopping. In contrast to web search, the retrieved results in product search not only need to be relevant but also shou…