Paper Info
Title
A multimodal query expansion based on genetic programming for visually-oriented e-commerce applications.
Abstract
An application of Genetic Programming where the results are better than previous results found in literature.A comparison of a Genetic Programming solution with two other learning-to-rank techniques: RankSVM and Random Forests.A multimodal expansion uses the initial query to infer other attributes that are relevant to the query.A new approach for visually-oriented e-commerce applications.A solution useful when the user is searching for products such as clothing, shoes, handbags, watches, and accessories.A solution that allows finding relevant products related to an image query even though their image representation is not similar to the query. We present a novel multimodal query expansion strategy, based on genetic programming (GP), for image search in visually-oriented e-commerce applications. Our GP-based approach aims at both: learning to expand queries with multimodal information and learning to compute the \"best\" ranking for the expanded queries. However, different from previous work, the query is only expressed in terms of the visual content, which brings several challenges for this type of application. In order to evaluate the effectiveness of our method, we have collected two datasets containing images of clothing products taken from different online shops. Experimental results indicate that our method is an effective alternative for improving the quality of image search results when compared to a genetic programming system based only on visual information. Our method can achieve gains varying from 10.8% against the strongest learning-to-rank baseline to 54% against an adhoc specialized solution for the particular domain at hand.
Year
DOI
Venue
2016
10.1016/j.ipm.2016.03.001
Inf. Process. Manage.
Keywords
Field
DocType
Content-based image retrieval,Multimodal query expansion,Genetic programming,E-commerce
Data mining,Query language,Computer science,Web query classification,Genetic programming,Ranking (information retrieval),Artificial intelligence,Query optimization,Web search query,Information retrieval,Query expansion,Machine learning,Content-based image retrieval
Journal
Volume
Issue
ISSN
52
5
0306-4573
Citations 
PageRank 
References 
5
0.41
35
Authors
5
Name
Order
Citations
PageRank
Patricia Correia Saraiva1151.32
Joao M. B. Cavalcanti21379.72
Edleno Silva de Moura398875.44
Marcos André Gonçalves42740191.03
Ricardo da Silva Torres578761.46