output
20162025
most citedNeural Rating Regression with Abstractive Tips Generation for Recommendation

306 citations

Showing cs.IRShow all

9 papers · 1 filter

cs.IR20213 cited

SearchGCN: Powering Embedding Retrieval by Graph Convolution Networks for E-Commerce Search

Xinlin Xia, Shang Wang, Han Zhang +5

Graph convolution networks (GCN), which recently becomes new state-of-the-art method for graph node classification, recommendation and other applications, has not been successfully…

cs.IR202124 cited

Joint Learning of Deep Retrieval Model and Product Quantization based Embedding Index

Han Zhang, Hongwei Shen, Yiming Qiu +6

Embedding index that enables fast approximate nearest neighbor(ANN) search, serves as an indispensable component for state-of-the-art deep retrieval systems. Traditional approaches…

cs.IR20211 cited

From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search

Rui Li, Yunjiang Jiang, Wenyun Yang +7

We introduce deep learning models to the two most important stages in product search at JD.com, one of the largest e-commerce platforms in the world. Specifically, we outline the d…

cs.IR2021

User-Inspired Posterior Network for Recommendation Reason Generation

Haolan Zhan, Hainan Zhang, Hongshen Chen +4

Recommendation reason generation, aiming at showing the selling points of products for customers, plays a vital role in attracting customers' attention as well as improving user ex…

cs.IR2020123 cited

Neural Interactive Collaborative Filtering

Lixin Zou, Long Xia, Yulong Gu +4

In this paper, we study collaborative filtering in an interactive setting, in which the recommender agents iterate between making recommendations and updating the user profile base…

cs.IR20204 cited

Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning

Han Zhang, Songlin Wang, Kang Zhang +5

Nowadays e-commerce search has become an integral part of many people's shopping routines. Two critical challenges stay in today's e-commerce search: how to retrieve items that are…