Paper Info
Title
Recommending Music Based on Probabilistic Latent Semantic Analysis on Korean Radio Episodes.
Abstract
Recommending music that satisfies the user's taste has been a challenging problem. Previous works on music recommendation system focused on the user's purchase history or the content of the music. In this paper, we propose a music recommendation system purely based on analyzing textual input of the users. We first mine a large corpus of Korean radio episodes, which is written by the listener. Each episode is composed of a personal story and a song request which we assume to be somehow related to the story. We then performing probabilistic Latent Semantic Analysis (pLSA) to find similar documents and recommend music that are associated to those documents. We evaluate our system by computing the mean reciprocal rank and mean average precision, which are both conventional metrics in evaluating information retrieval systems. The result shows that music similarity and document similarity are closely correlated, and thus it is possible to recommend music purely based on text analysis.
Year
DOI
Venue
2013
10.1109/IIH-MSP.2013.123
IIH-MSP
Keywords
Field
DocType
mean reciprocal rank,music similarity,information retrieval system,average precision,personal story,korean radio episodes,challenging problem,probabilistic latent semantic analysis,document similarity,korean radio episode,recommending music,music recommendation system,text mining
Recommender system,Information retrieval,Computer science,Mean reciprocal rank,Natural language processing,Probabilistic latent semantic analysis,Artificial intelligence,Document similarity
Conference
Citations 
PageRank 
References 
3
0.40
12
Authors
2
Name
Order
Citations
PageRank
Ziwon Hyung1271.96
Kyogu Lee226338.85