A Novel Approach to Quantify Novelty Levels Applied on Ubiquitous Music Distribution

In order to take advantage and profit with the popularization of digital music, companies started marketing licensed content on high-storage portable media players. The introduction of wireless technology in such players motivates new business opportunities where music distribution is ubiquitous. However, in such high-supply scenario, consumers may have difficulties to find interesting content. In such context, music recommender systems assist consumers in identifying their preferences and in supporting content searches. An important feature in such market is the low attention given to new music styles, what increases the promotion costs. In order to assist consumers who, positive or negatively, pay attention to such novelty factor, this work proposes a novel method to estimate music preference profiles based on acoustic similarity measures. Such profiles are learnt by an artificial neural network, named self-organizing novelty detection neural network architecture (SONDE), which classifies and quantifies the novelty level of music titles regarding the user profile. Based on novelty levels, we suggest a discount rate model to support promotion strategies. The proposed method is evaluated by simulating some scenarios.