A Web-Based Approach to Assessing Artist Similarity using Co-Occurrences

In this paper, we present a similarity measure for music artists based on search results of Google queries. Co-occurrences of artist names on web pages are analyzed to measure how often two artists are mentioned together on the same web page. We estimate conditional probabilities using the extracted page count. These conditional probabilities give a similarity measure which is evaluated using a data set containing 224 artists from 14 genres. For evaluation, we use two different methods, intra-/intergroup-similarities and k-Nearest Neighbors classification. Furthermore, a confidence filter and combinations of the results gained from three different query settings are tested. It is shown that these enhancements can raise the performance of our similarity measure. Comparing our results to those of similar approaches show that our approach, though being quite simple, performs well and can be used as a similarity measure that incorporates “social knowledge”.