A Hybrid Music Recommendation System Using Collaborative Filtering and Content Analysis

Research Overview

The purpose of this research is to develop a hybrid music recommendation system that is customized to suit Korea’s music market and its music listeners. The technological advances in today’s hardware and the rapid development of the internet has lead to millions of songs being available online. However, paradoxically, there is less music for users now that more is available because of information overload. To solve this problem, music recommender systems filter out only the items that are relevant and of value to the user.

Figure 1. Concept of the recommender system

The basic concept of the system proposed in this research is to use experts when providing recommendations to ‘novices’. A broad definition of an expert would be a person who is knowledgeable in a certain area, while a novice is one who lacks such knowledge. Thus, it is only natural that knowledge is transferred from experts to novices. In this regard, we use users who are considered experts in their domain to provide recommendations to novices in those domains.

In this recommender system, each user has a domain in which he/she is a novice and an expert. An expert is defined as a user whose item consumption is skewed, or focused, on a certain set of similar items. Likewise, a user is a novice in areas where the consumption rate is low. Taking movies as an example, it is only reasonable that a person who enjoys watching sci-fi movies can provide helpful recommendations to a user who usually watches drama but occasionally finds some sci-fi movies engaging.

Thus, in order to find experts, the items are placed in an N-dimensional space, so that similar items are placed together and dissimilar items are apart. Similar items are then clustered, which define the areas that a user can be an expert or novice in. Next, each user is analyzed to see the distribution among the clusters, or areas, that the consumed items are in and are labeled as experts for specific clusters, accordingly, as shown in Figure 1. When providing recommendations for a novice, the experts of the cluster that the user is a novice in are used to generate novel and relevant recommendations.


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Project Members

Kibeom Lee, Ziwon Hyung, Sangmin Lee, Yekyung Kim

Funding Agency

National Research Foundation