Paper Info
Title
Neural Compatibility Ranking for Text-based Fashion Matching
Abstract
When shopping for fashion, customers often look for products which can complement their current outfit. For example, customers want to buy a jacket which can go well with their jeans and sneakers. To address the task of fashion matching, we propose a neural compatibility model for ranking fashion products based on the compatibility matching with the input outfit. The contribution of our work is twofold. First, we demonstrate that product descriptions contain rich information about product comparability which has not been fully utilized in the prior work. Secondly, we exploit such useful information from text data by taking advantages of semantic matching and lexical matching both of which are important for fashion matching. The proposed model is evaluated on a real-world fashion outfit dataset and achieves the state-of-the-art results by comparing to the competitive baselines. In the future work, we plan to extend the model by incorporating product images which are the major data source in the prior work on fashion matching.
Year
DOI
Venue
2019
10.1145/3331184.3331365
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
Keywords
Field
DocType
fashion compatibility, neural information retrieval
Data source,Data mining,Ranking,Information retrieval,Compatibility (mechanics),Computer science,Baseline (configuration management),Exploit,Comparability,Semantic matching
Conference
ISBN
Citations 
PageRank 
978-1-4503-6172-9
2
0.36
References 
Authors
0
4
Name
Order
Citations
PageRank
Suthee Chaidaroon1182.31
Yi Fang237932.01
Min Xie321711.60
Alessandro Magnani4114.17