A unified music recommender system using listening habits and semantics of tags. (1st January 2014)

• Record Type: Journal Article
• Authors: Kim, Hyon Hee; Kim, Donggeon; Jo, Jinnam
• Is Part Of: International journal of intelligent information and database systems. Volume 8:Number 1(2014)
• Page Start: 14
• Page End: 30
• Publication Date: 2014-01-01
• Subjects: music recommendation -- tag ontology -- intelligent information and database systems -- emotion ontology -- listening habits; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

In this paper, we propose a unified music recommender system using both listening habits and semantics of tags in a social music site. Most commercial music recommender systems recommend music items based on the number of plays, or explicit ratings of a song. However, these approaches have some difficulties in recommending new items with only a few ratings, or recommending items to new users with little information. To resolve the problem, UniTag ontology is developed, which defines the meaning and the weighted score of tags. User profiles are created by combining the score of tags and the number of plays, and a collaborative filtering algorithm is executed. For performance evaluation, precisions, recalls, and F-measures are measured using the listening habits-based recommendation, the tag score-based recommendation, and the unified recommendation, respectively. Our experiments show that the proposed approach outperforms the other two approaches in terms of all the evaluation metrics.