Music recommendation model based on user listening behavior and utility based preference scoring

Recommending the most appropriate music is one of the most studied fields in the contest of Music Information Retrieval. Music Recommendation modules often take note of the users music preferences when it comes to recommending music. In this study, approaches such as Music Similarity, have also been applied during the recommendation phase. The study made use of normalized acoustic features extracted using MIR tools MARSYAS 0.5.0 alpha 1 and Audio 1.0.4 and utility based preference scoring to find relevant music to be used as recommendations. Using this approach, the study was able to come up with an average True-Positive rating of 54.43% in determining the songs the user will select for the month given previous months data. This study made use of a recommendation formula that can be used for future studies. Some examples could be a different set of similarity measures used, more computational functions to use as a basis for recommendation, as well as changing constant values used throughout the computational functions used during the research. Applying suggestions for measuring utility can also be used for further studies who wish to go into dynamic and more active recommendation models

Validating the stable clustering of songs in a structured 3D SOM

A structured 3D SOM is an extension of a Self-Organizing Map from 2D to 3D in such a way that a pre-defined structure is built into the design of the 3D map. The structured 3D SOM is a 3×3×3 structure that has a distinct core cube in the center and exterior cubes around the core. The current application of the structured SOM, as a digital music archive, only uses the 8 corner cubes among the 26 exterior cubes. Given that the SOM has a built-in structure, the SOM learning algorithm is modified to include a four-phase learning and labeling phase. The first phase is meant to position the music files in their general locations within the core cube. The second phase positions the music files in their respective corner cubes according to their music genre. The second phase is therefore a semi-supervised version of the SOM algorithm which leads to the stability of the trained SOM in terms of the general distribution of the music files in the core cube. The third phase does a fine adjustment of the weight vectors in the core cube and finalizes the training of the 3D SOM. The final fourth phase is the labeling of the core cube and the association (uploading) of music files to specific nodes in the core cube. Based on the pre-defined structure of the 3D SOM, a precise measure is developed to measure the quality of the resulting trained SOM (in this case, the music archive), as well as the quality of the different categories/genres of music albums based on a novel measure of the distortion values of music files with respect to their respective music genres. © 2016 IEEE