Multimedia recommendation using Word2Vec-based social relationship mining

• Published in: Multimedia tools and applications Vol. 80; no. 26-27; pp. 34499 - 34515
• Main Authors: Baek, Ji-Won, Chung, Kyung-Yong
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-11-2021; Springer Nature B.V
• Subjects: Computer Communication Networks; Computer Science; Data mining; Data Structures and Information Theory; Metadata; Multimedia; Multimedia Information Systems; Performance evaluation; Preferences; Recommender systems; Special Purpose and Application-Based Systems; Multimedia; Word2vec; Social relationship mining; Recommendation
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-019-08607-9

This study proposes the multimedia recommendation method using Word2Vec-based social relationship mining. This is to analyze users with a similar tendency on the basis of the keywords related to multimedia content and sentiment words of comments, to build a trust relationship, and to recommend multimedia. In order to solve the problem of data sparsity, metadata of multimedia content are collected and then are clustered by genre. User’s evaluate a preference for multimedia content. With the use of evaluation data, the attributes preferred by users are predicted. In terms of propensities, the sentiment words in users comments are classified by SVM on the basis of sentiment dictionary. The classified sentiment words are presented in vector with the use of Word2Vec. In terms of the vector of sentiment words, the dynamic relationship between users of words in the same preference by the similarity using the distance scale. It helps to build a trust relationship between users with preferences that can change with a lapse of time. Accordingly, multimedia content are recommended to users with a similar tendency. In terms of performance evaluation, F-measure is compared with the uses of precision and recall for a recommendation. As the result of evaluation, the social relationship mining method is evaluated to be better than explicit and implicit recommendation methods. With the proposed method, it is possible to search with metadata of content and make a intelligent recommendation explicitly and implicitly according to user’s tendency.