Automatic Generation of Social Tags for Music Recommendation
نویسندگان
چکیده
Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of “Web2.0” recommender systems, allowing users to generate playlists based on use-dependent terms such as chill or jogging that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly from MP3 files. Using a set of boosted classifiers, we map audio features onto social tags collected from the Web. The resulting automatic tags (or autotags) furnish information about music that is otherwise untagged or poorly tagged, allowing for insertion of previously unheard music into a social recommender. This avoids the ”cold-start problem” common in such systems. Autotags can also be used to smooth the tag space from which similarities and recommendations are made by providing a set of comparable baseline tags for all tracks in a recommender system.
منابع مشابه
Automatic Hashtag Recommendation in Social Networking and Microblogging Platforms Using a Knowledge-Intensive Content-based Approach
In social networking/microblogging environments, #tag is often used for categorizing messages and marking their key points. Also, since some social networks such as twitter apply restrictions on the number of characters in messages, #tags can serve as a useful tool for helping users express their messages. In this paper, a new knowledge-intensive content-based #tag recommendation system is intr...
متن کاملSMART: Semi-Supervised Music Emotion Recognition with Social Tagging
Music emotion recognition (MER) aims to recognize the affective content of a piece of music, which is important for applications such as automatic soundtrack generation and music recommendation. MER is commonly formulated as a supervised learning problem. In practice, except for Pop music, there is little labeled data in most genres. In addition, emotion is genre specific in music and thus the ...
متن کاملPersonalized Music Recommendation by Mining Social Media Tags
Over the past few years, the recommender system has been proposed as a critical role to help users choose the preferred product from a massive amount of data. For music recommendation, most recent recommender systems made attempts to associate music with the user’s preferences primarily based on user ratings. However, this kind of recommendation mechanism encounters the problem called rating di...
متن کاملListener-Aware Music Recommendation from Sensor and Social Media Data
Music recommender systems are lately seeing a sharp increase in popularity due to many novel commercial music streaming services. Most systems, however, do not decently take their listeners into account when recommending music items. In this note, we summarize our recent work and report our latest findings on the topics of tailoring music recommendations to individual listeners and to groups of...
متن کاملAutotagging Music Using Supervised Machine Learning
Social tags are an important component of “Web2.0” music recommendation websites. In this paper we propose a method for predicting social tags using audio features and supervised learning. These automatically-generated tags (or “autotags”) can furnish information about music that is untagged or poorly tagged. The tags can also serve to smooth the tag space from which similarities and recommenda...
متن کامل