A multimodal query expansion based on genetic programming for visually-oriented e-commerce applications
作者：
Highlights：
• 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.
摘要
•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.
论文关键词：Content-based image retrieval,Multimodal query expansion,Genetic programming,E-commerce
论文评审过程：Received 6 November 2014, Revised 23 February 2016, Accepted 7 March 2016, Available online 20 April 2016, Version of Record 22 July 2016.
论文官网地址：https://doi.org/10.1016/j.ipm.2016.03.001
