text: While prior work on context-based music recommendation focused on fixed set of contexts (e.g. walking, driving, jogging), we propose to use multiple sensors and external data sources to describe momentary (ephemeral) context in a rich way with a very large number of possible states (e.g. jogging fast along in downtown of Sydney under a heavy rain at night being tired and angry). With our approach, we address the problems which current approaches face: 1) a limited ability to infer context from missing or faulty sensor data; 2) an inability to use contextual information to support novel content discovery.
  @cite_11: 
  @cite_7: 
  @cite_9: Mobile devices such as smart phones are becoming popular, and realtime access to multimedia data in different environments is getting easier. With properly equipped communication services, users can easily obtain the widely distributed videos, music, and documents they want. Because of its usability and capacity requirements, music is more popular than other types of multimedia data. Documents and videos are difficult to view on mobile phones' small screens, and videos' large data size results in high overhead for retrieval. But advanced compression techniques for music reduce the required storage space significantly and make the circulation of music data easier. This means that users can capture their favorite music directly from the Web without going to music stores. Accordingly, helping users find music they like in a large archive has become an attractive but challenging issue over the past few years.
  @cite_0: 
  @cite_2: The amount of music consumed while on the move has been spiraling during the past couple of years, which requests for intelligent music recommendation techniques. In this demo paper, we introduce a context-aware mobile music player named "Mobile Music Genius" (MMG), which seamlessly adapts the music playlist on the fly, according to the user context. It makes use of a comprehensive set of features derived from sensor data, spatiotemporal information, and user interaction to learn which kind of music a listeners prefers in which context. We describe the automatic creation and adaptation of playlists and present results of a study that investigates the capabilities of the gathered user context features to predict the listener's music preference.
  @cite_5: As the World Wide Web becomes a large source of digital music, the music recommendation system has got a great demand. There are several music recommendation systems for both commercial and academic areas, which deal with the user preference as fixed. However, since the music preferred by a user may change depending on the contexts, the conventional systems have inherent problems. This paper proposes a context-aware music recommendation system (CA-MRS) that exploits the fuzzy system, Bayesian networks and the utility theory in order to recommend appropriate music with respect to the context. We have analyzed the recommendation process and performed a subjective test to show the usefulness of the proposed system.
  @cite_15: 
  @cite_10: We present a system to automatically generate soundtracks for user-generated outdoor videos (UGV) based on concurrently captured contextual sensor information with mobile apps for the ACM Multimedia 2012 Google challenge: Automatic Music Video Generation. Our method addresses the use case of making "a video much more attractive for sharing by adding a matching soundtrack to it." Our system correlates viewable scene information from sensors with geographic contextual tags from OpenStreetMap. The co-occurance of geo-tags and mood tags are investigated from a set of categories of the web site Foursquare.com and a mapping from geo-tags to mood tags is obtained. Finally, a music retrieval component returns music based on matching mood tags. The experimental results show that our system can successfully create soundtracks that are related to the mood and situation of UGVs and therefore enhance the enjoyment of viewers. Our system sends only sensor data to a cloud service and is therefore bandwidth efficient since video data does not need to be transmitted for analysis.
  @cite_17: In this paper, we present a new user heartbeat and preference aware music recommendation system. The system can not only recommend a music playlist based on the user’s music preference but also the music playlist is generated based on the user’s heartbeat. If the user’s heartbeat is higher than the normal heartbeat which is 60-100 beats per minutes (age 18 and over) or 70-100 beats per minutes (age 6-18), the system generates a user preferred music playlist using Markov decision process to transfer the user’s heartbeat back to the normal range with the minimum time cost; if the user’s heartbeat is normal, the system generates a user preferred music playlist to keep the user’s heartbeat within the normal range; If the user’s heartbeat is lower than the normal heartbeat, the system generates a user preferred music playlist using Markov decision process to uplift the user’s heartbeat back to the normal range with the minimum time cost.
summary: With improvements in smartphone technology, there is a lot of potential for using rich contextual information to improve recommendations, in particular considering that people prefer to listen to different music in different contexts @cite_11 @cite_7 @cite_15 . Among the first to propose a context-aware music recommendation system are @cite_5 . They used weather data (from sensors and external data sources), and user information, to predict the appropriate music genre, tempo, and mood. Music can also be recommended based on user's heart beat to bring its rate to a normal level @cite_17 ; activities detected automatically (e.g. running, walking, sleeping, working, studying, and shopping) @cite_2 ; driving style, road type, landscape, sleepiness, traffic conditions, mood, weather, natural phenomena @cite_9 ; and emotional state to help to transition to a desired state @cite_0 . Soundtracks have also been recommended for smartphone videos based on location (using GPS and compass data for orientation), and extra information from 3rd party services such as Foursquare @cite_10 .