Bayesian Learning through Weight of Listener’s Prefered Music Site for Music Recommender System

다국어 초록 (Multilingual Abstract)

Along with the spread of digital music and recent growth in the digital music industry, the demands for music recommender are increasing. These days, listeners have increasingly preferred to digital real-time streamlining and downloading to listen to music because it is convenient and affordable for the listeners to do that. We use Bayesian learning through weight of listener’s prefered music site such as Melon, Billboard, Bugs Music, Soribada, and Gini. We reflect most popular current songs across all genres and styles for music recommender system using user profile. It is necessary for us to make the task of preprocessing of clustering the preference with weight of listener’s preferred music site with popular music charts. We evaluated the proposed system on the data set of music sites to measure its performance. We reported some of the experimental result, which is better performance than the previous system.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Related works
• 2.1 Collaborative Filtering(CF)
• 2.2 Clustering
• 2.3 Bayesian Networks(BNs)
• 3. Our proposal for a Music Recommender System
• 3.1 Clustering with Weight of Listener’s Prefered Music Site
• 4. The Environment of Implementation and Experiment and Evaluation
• 4.1. Experimental Data for Evaluation
• 4.2 Experiment and Evaluation
• 5. Conclusions
• References