Personalized Music Recommendation Based On Context-awareness

With the rapid development of Web2.0, the amount of Internet data in Shanghai can be retrieved and shared, but it becomes more and more difficult for users to search and retrieve the content and information that meet their needs. Although search engines can help users search for keywords to search their own interest. However, the search engine cannot solve all the problems of information search. Users may not be accurate clear,search engines can help users to retrieve specific content, but such as movies, music and books of this information, the user is not really clear about your needs, will not clear the search keywords. At this point, the user needs not only information search, but also information discovery. Personalized recommendation system is more suitable for users of movies, music, books, etc.With the rapid development of information industry, people’s life has been completely facilitated, at the same time, it also brings disadvantages of information overload.Facing the huge amount of information, it is difficult for people to identify and obtain valuable content resources. As an effective way of information filtering, personalized recommendation technology has attracted more and more attention. Especially in recent years, with the rapid breakthrough of artificial intelligence, pattern recognition,distributed storage, cloud computing and large data mining technology, personalized recommendation technology has been further developed. Personalized recommendation technology can extract the information of the user’s characteristics, attributes, behavior patterns and social preferences, and can search out the content of the user’s real interest from the vast amount of information. However, in the field of recommended systems,traditional personalized recommendation system only considers the relationship between the user and the program, and the user has no impact on the situation into consideration, the final presentation of the recommendation results to achieve the desired.In recent years, there have got a variety of music recommendation websites. These sites recommend music to the user based on user behavior data, to some extent, help users find more music. At present, most of the music recommendation engines are built on collaborative filtering recommendation mechanism. In this paper, we propose in the application of the user’s situational factors to the existing personalized music recommendation system. Through the establishment of a user network music scene model, to provide users with personalized music recommendation, can enhance the user satisfaction of music recommendation. Recent advances in mobile devices and sensing capabilities, which enrich the context aware information and mobile device information,also provide technical feasibility for the music context aware recommended system.User context information is a major factor in the process of recommendation. Through the research methods of a series of users, analysis of music has recommended the needs of users, the user context is divided into static and dynamic situation situation, impact on the user selected music, through data analysis, the establishment of user network music situation model, and this model should be used in the existing recommendation systems, to provide more in line with their interest and like the music recommendation service for users.