Abstract
In this paper, we present an approach for musical artist recommendation based on Self-Organizing Maps (SOMs) of artist reviews from Amazon web site. The Amazon reviews for the artists are obtained using the Amazon web service interface and stored in the form of textual documents that form the basis for the formation of the SOMs. The idea is to spatially organize these textual documents wherein similar documents are located nearby. We make an attempt to exploit the similarities between different artist reviews to provide insights into similar artists that can be used in a recommendation service. We introduce the concept of a modified weighting scheme for text mining in the musical domain and demonstrate its role in improving the quality of the recommendations. Finally, we present results for a list of around 400 musical artists and validate them using recommendations from a popular recommendation service.
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Vembu, S., Baumann, S. (2005). A Self-Organizing Map Based Knowledge Discovery for Music Recommendation Systems. In: Wiil, U.K. (eds) Computer Music Modeling and Retrieval. CMMR 2004. Lecture Notes in Computer Science, vol 3310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31807-1_9
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DOI: https://doi.org/10.1007/978-3-540-31807-1_9
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