Publications
CONFERENCE (INTERNATIONAL) Learning to Rank-based Approach for Movie Search by Keyword Query and Example Query
Kosuke Kurihara, Yoshiyuki Shoji (Aoyama Gakuin univ.), Sumio Fujita, Martin J. Dürst (Aoyama Gakuin univ.)
The 23rd International Conference on Information Integration and Web Intelligence (iiWAS 2021)
November 29, 2021
This paper proposes a method for ranking movies by keyword queries and examples using machine learning techniques that analyze actual data from the online movie review site. Existing search methods cannot rank movies in “surprising order” for the keyword query “surprising.” People and critics created many “My best surprising movies” rankings on the web. Our proposed method uses a LambdaMART, one of the mainstream Learning to Rank techniques, to learn these personalized rankings and sort the movies through the viewpoint represented by a given query. To accept more complex information needs, we diverted the learning results to a search-by-example algorithm that enables users to input examples, such as “surprising movies like The Usual Suspects or Fight Club.” The experiment using the personal ranking data from the personal content curation service in Yahoo! Movies Japan suggests two findings: direct learning of personal ranking does not improve search performance, and the search-by-example-based application increases user satisfaction.
Paper :
Learning to Rank-based Approach for Movie Search by Keyword Query and Example Query
(external link)