Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
CARD: a decision-guidance framework and application for recommending composite alternatives
Proceedings of the 2008 ACM conference on Recommender systems
Emotion-based music recommendation by affinity discovery from film music
Expert Systems with Applications: An International Journal
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A novel method for personalized music recommendation
Expert Systems with Applications: An International Journal
An intelligent music playlist generator based on the time parameter with artificial neural networks
Expert Systems with Applications: An International Journal
Music Recommendation Using Content and Context Information Mining
IEEE Intelligent Systems
Music emotion classification and context-based music recommendation
Multimedia Tools and Applications
Hybrid recommendation approaches for multi-criteria collaborative filtering
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Recommending appropriate music to users has always been a difficult task. In this paper, we propose a novel method in recommending music by analyzing the textual input of users. To this end, we mine a large corpus of documents from a Korean radio station's online bulletin board. Each document, written by the listener, is composed of a song request associated with a brief, personal story. We assume that such stories are closely related with the background of the song requests and thus, our system performs text analysis to recommend songs that were requested from other similar stories. We evaluate our system using conventional metrics along with a user evaluation test. Results show that there is close correlation between document similarity and song similarity, indicating the potential of using text as a source to recommending music.