One of the major problems of recommender systems in general, and music recommender systems in particular is the cold start problem, i.e., when a new user registers to the system or a new item is added to the catalog and the system does not have sufficient data associated with these items/users. In such a case, the system cannot properly recommend existing items to a new user (new user problem) or recommend a new item to the existing users
Extract audio metadata features from the audio signals and use content-based learning of the user interest, and user's friends interest in order to effect recommendation.
- Stream efficiently based on the network bandwidth
- State-of-the-art streaming player
- Performant search engine for fast searching
- Authentication via social auth/Oauth 2.0
- Rich features for music player
- Full screen player support
- Support for hotkeys while playing music
- Separate recommendation zone with recommendations on the fly using ML algorithms with good accuracy
