The document discusses a music recommendation system project that uses content-based filtering and collaborative filtering techniques. Content-based filtering extracts features from songs to find similar songs based on acoustic content. Collaborative filtering matches users based on similar tastes and ratings to generate recommendations. The project has developed a website using Ruby on Rails for the frontend and Python for the backend. Current work involves completing the collaborative filtering approach and exploring query by humming algorithms.

1. Music Recommendation SystemContentIntroduction

2. Progress of work

3. Work done

4. Algorithms and Technical details

5. Dataset

6. ReferencesBTP Report => 3Panel No => 5Group No => G6Members => dinesh singh yadav (200601026) Vinay Kuamr (200601102) Faculty Advisor => Dr. Vikram Pudi IntroductionRecommendation system is to recommend the related items to the users interest. Basically the main goal of this system is to propose to user interesting music, to discover, including unknown artists, their popular available tracks based on users’ musical taste. Music domain is somewhat different than other domains as implicit ratings are not collected in the terms of ratings but in the terms of playing, skipping, or stopping recommended track. The work is focused on presenting to a user a list of artists, or creating the ordered set of tracks (personalized playlist) based on common approaches like collaborative and content-based filtering. Typically, a recommender system compares the user's profile to some reference characteristics, and seeks to predict the 'rating' that a user would give to an item they had not yet considered.Work Done

7. Followings are the different techniques have been used to get the top-N recommended items or songs for the users.