From Sensors to Songs: A Learning-Free Novel Music Recommendation System using Contextual Sensor Data

Traditional approaches for music recommender systems face the known challenges of providing new recommendations that users perceive as novel and serendipitous discoveries. Even with all the music content available on the web and commercial music streaming services, discovering new music remains a time consuming and taxing activity for the average user. The goal for our proposed system is to provide novel music recommendations based on contextual sensor information. For example, contextual place information can be inferred with intelligent use of techniques such as geo-fencing and using lightweight sensors like accelerometers and compass to monitor location. The inspiration behind our system is that music is not in the past, neither in the future, but rather enjoyed in the present. For this reason, the system does not rely on learning the user’s listening history. Raw sensor data is fused with information from the web, passed through a cascade of Fuzzy Logic models to infer the user’s context, which is then used to recommend music from an online music streaming service (SoundCloud) after filtering out songs based on genre preferences that the user dislikes. This paper motivates and describes the design for a mobile application along with a description of tests that will be carried out for validation.