Social mix: automatic music recommendation and mixing scheme based on social network analysis

General preferences for music change over time. Moreover, music preferences depend on diverse factors, such as language, people, location, and culture. This dependency should be carefully considered to provide satisfactory music recommendations. Presently, typical music recommendations simply involve providing a list of songs that are then played sequentially or randomly. Recently, there has been an increasing demand for new music recommendation and playback methods. In this paper, we propose a scheme for recommending music automatically by considering both personal and general musical predilections, and for blending such music into a mixed clip for seamless playback. For automatic music recommendations, we first analyze social networks to identify a general predilection for certain music genres that depends on time and location. Songs that are generally preferred within a certain time period and location are identified through statistical analysis. This is done by analyzing, filtering, and storing massive social network streams into our own database in real time. In addition, a personal predilection for certain music genres can be inferred by analyzing similar user relationships in social network services. We selected such music based on instant graphs that are generated by user relationships and underlying music information. After the songs are selected, an automatic music mixing method is used to blend those songs into a continuous music clip. We implemented a prototype system and experimentally confirmed that our scheme provides satisfactory results.