output
20122026
most citedOn Application of Learning to Rank for E-Commerce Search

143 citations

Showing 2019Show all

6 papers · 1 filter

cs.CV20199 cited

In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label Distillation

Ye Yuan, Wuyang Chen, Yang Yang +1

The comparative losses (typically, triplet loss) are appealing choices for learning person re-identification (ReID) features. However, the triplet loss is computationally much more…

cs.LG201935 cited

Self-attention with Functional Time Representation Learning

Da Xu, Chuanwei Ruan, Sushant Kumar +2

Sequential modelling with self-attention has achieved cutting edge performances in natural language processing. With advantages in model flexibility, computation complexity and int…

cs.LG201972 cited

Product Knowledge Graph Embedding for E-commerce

Da Xu, Chuanwei Ruan, Evren Korpeoglu +2

In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce. We define the key…

cs.LG201911 cited

Generative Graph Convolutional Network for Growing Graphs

Da Xu, Chuanwei Ruan, Kamiya Motwani +3

Modeling generative process of growing graphs has wide applications in social networks and recommendation systems, where cold start problem leads to new nodes isolated from existin…

cs.SI20191 cited

Signed Link Prediction with Sparse Data: The Role of Personality Information

Ghazaleh Beigi, Suhas Ranganath, Huan Liu

Predicting signed links in social networks often faces the problem of signed link data sparsity, i.e., only a small percentage of signed links are given. The problem is exacerbated…

cs.IR2019143 cited

On Application of Learning to Rank for E-Commerce Search

Shubhra Kanti Karmaker Santu, Parikshit Sondhi, ChengXiang Zhai

E-Commerce (E-Com) search is an emerging important new application of information retrieval. Learning to Rank (LETOR) is a general effective strategy for optimizing search engines,…