2. Progress of work

3. Work done

4. Algorithms and Technical details

5. Dataset

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

9. Collaborative filtering is in the verge of complete and will be completed by the time of Demo. We are studying the algorithms of Query by Humming and most probably it will be listed for the next work. Few of the papers are being followed for this process which seems a bit difficult to get good accuracy because this is totally new and advance work which is still a research topic. Right now a site has been developed which acts on client side. Site is still a simple and completes basic needs but not much interactive. For more attractiveness it needs a little settlement for CSS and JavaScript which will be left for further improvement time. Site has been made in Ruby on Rails and the back end has been made in Python. Site is having search option for user in music and some other events. User will be redirected to search result of search query by user. Whatever song is played by the user will be used to get all other related songs of that category or type. A list will be displayed with a sequence of most similar song with top rated for that user. Web site is developed via MVC model which is quite efficient in giving results as it makes efficient way to interact with data models and gives the results.

10. We have hosted an open source project Simreco at Github and all of our work is online and free to extend.

11. Project URl http://github.com/dinesh/simreco/tree/master

12. Git-clone url git://github.com/dinesh/simreco.git

13. Web Interface- As it’s been explained in above paragraph that web interface has been developed with MVC architecture which is most popular today as most of the sites are using MVC model based framework, AJAX, JavaScript libraries. Site has been intended to provide publically so that users profile can be got easily when they rate and tell us their interest. It is having a flash player which will play the audio file on client side. As we need a very good streaming of audio file which needs an efficient way of implementation of model which will not make load on site while increasing the registration of users day-by-day.

14. Implementation of Web-based radio using flash - Internet radio has wider audience and eliminates the need of downloading, managinglarge audio libraries. It will be a streaming media server integrated with flash front-end. User-client can give feedback as thumps-up, thumps-down, love, stop, skip and ban. It should also learn the pattern of user listening habits and incorporate the feedback.

15. Gathering implicit feedback in terms of skipping, playing, stopping the songs. It is defined earlier to increase the recommendations using users’ personal feedback.

16. Improvement of recommendation Algorithm - From the previous results its improvement has been increased expectedly enough. New addition of content based filtering and collaborative filtering is efficient in their own way more than the previous algorithms we implemented.

17. Ability to recommend by artist, user-profiles as seedsIt defines the ability to generate the recommendations based on artists,user-profiles by extending the recommendation algorithm for searching similar artist and knn-Neighbor-search algorithm.

18. Crawling/ mining for new music-

19. Music content is growing like mushrooms and many new technologies podcasts, rss feeds, foaf profiles, weblogs and mp3-blogs are publishing updates on their site. The recommendation System should make use of these services to regularly find new music, events and releases of users favorites artists.

22. Implicit based-