1. Music Recommendation A Data Mining Approach Daniel McEnnis 2nd year PhD Daniel McEnnis 2nd year PhD

2. Overview  High level overview  Toolkit Improvements  Experiments  Evaluation  Algorithms research  Data  Future work  High level overview  Toolkit Improvements  Experiments  Evaluation  Algorithms research  Data  Future work

3. Project Goals  Integrate social information  Make algorithms ‘culturally aware’  Implement existing algorithms  Systematic evaluation framework  Integrate social information  Make algorithms ‘culturally aware’  Implement existing algorithms  Systematic evaluation framework

4. Similarity Algorithms  Create new relations based on some aspect of similarity  6 different varieties of similarity  Each algorithm can use one of 6 distance functions  Create new relations based on some aspect of similarity  6 different varieties of similarity  Each algorithm can use one of 6 distance functions

5. Aggregator Algorithms  Takes data from one set of actors and moves it to another  6 different varierties  Each variety uses one of 7 aggregator functions  Basic building block of Graph-RAT applications  Takes data from one set of actors and moves it to another  6 different varierties  Each variety uses one of 7 aggregator functions  Basic building block of Graph-RAT applications

6. Graph Triples Census  Probable novel algorithm  Proof of Correctness Completed  Proof of Time Complexity Completed  Literature review in progress  Probable novel algorithm  Proof of Correctness Completed  Proof of Time Complexity Completed  Literature review in progress

7. SUCCESS!  Graph-RAT programming language now functioning  Graph-RAT integrates social, cultural, personal, and audio data into algorithms  Includes most commercial algorithms  Contains primitives for existing academic systems  Evaluation is entirely automated  Graph-RAT programming language now functioning  Graph-RAT integrates social, cultural, personal, and audio data into algorithms  Includes most commercial algorithms  Contains primitives for existing academic systems  Evaluation is entirely automated

8. PROBLEMS

9. Evaluation Exploration  9 types of music recommendation  Personalized versus generic  Open query versus targeted query  Dynamic versus static data  New music versus all music  9 types of music recommendation  Personalized versus generic  Open query versus targeted query  Dynamic versus static data  New music versus all music

10. Personalized Radio  Open query with personalized presentation  Static data vs dynamic data  New items prediction vs predict anything  Open query with personalized presentation  Static data vs dynamic data  New items prediction vs predict anything

11. Targeted Search  Not personalized  Similarity queries  Automatically generating targeted lists for a browsing hierarchy  New music vs all music  Static vs dynamic data  Not personalized  Similarity queries  Automatically generating targeted lists for a browsing hierarchy  New music vs all music  Static vs dynamic data

12. Personalized Tag Radio  Create a personalized play list matching a given query  New music vs all music  Static vs dynamic data  Create a personalized play list matching a given query  New music vs all music  Static vs dynamic data

13. Excluded Types  ‘Top 40’ prediction  Rendered obsolete by other types  ‘Top 40’ prediction  Rendered obsolete by other types

14. Existing Algorithms  Item-to-Item collaborative filtering  7 variations  User-to-user collaborative filtering  7 variations  Associative mining collaborative filtering  Direct machine learning playlist data  Direct machine learning audio data  Item-to-Item collaborative filtering  7 variations  User-to-user collaborative filtering  7 variations  Associative mining collaborative filtering  Direct machine learning playlist data  Direct machine learning audio data

15. Novel Algorithms  Machine learning over profile data  Machine learning over cultural and profile data  Machine learning on different concatenations  Audio  Playlist  Profile  Cultural  Machine learning over profile data  Machine learning over cultural and profile data  Machine learning on different concatenations  Audio  Playlist  Profile  Cultural

16. Initial Data  LiveJournal  Separating music data is difficult  No tag info or audio content  No enough musical data  LastFM by User  No audio content  Data cleaning is an issue  LiveJournal  Separating music data is difficult  No tag info or audio content  No enough musical data  LastFM by User  No audio content  Data cleaning is an issue

17. Current Data  40’s Jazz Recordings  1800 annotated recordings from 70 CDs  Covers nearly all 40’s popular music  LastFM by Song  Retrieves tag and user info by song  Data cleaning on user playcounts needed  40’s Jazz Recordings  1800 annotated recordings from 70 CDs  Covers nearly all 40’s popular music  LastFM by Song  Retrieves tag and user info by song  Data cleaning on user playcounts needed

18. Data Cleaning Tags  Polysemy  Synonomy  Disjoint  Hypersomny  Hyposomny  Initial algorithms developed  Polysemy  Synonomy  Disjoint  Hypersomny  Hyposomny  Initial algorithms developed

19. Future Work: Programming  Radically different programming environment  SQL  LINQ library package in C#  Radically different programming environment  SQL  LINQ library package in C#

20. Future Work: Scalability  Distributed SQL database implementation  Just-in-time compilation  Event-based recalculation of algorithm results  Parallel execution of algorithms  Multi-threaded algorithms  Distributed SQL database implementation  Just-in-time compilation  Event-based recalculation of algorithm results  Parallel execution of algorithms  Multi-threaded algorithms