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Music Recommendation A Data Mining Approach

Music Recommendation A Data Mining Approach. Daniel McEnnis 2nd year PhD. Overview. High level overview Toolkit Improvements Experiments Evaluation Algorithms research Data Future work. Project Goals. Integrate social information Make algorithms ‘culturally aware’

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Music Recommendation A Data Mining Approach

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

  2. Overview • 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

  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

  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

  6. Graph Triples Census • 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

  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

  10. Personalized Radio • 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

  12. Personalized Tag Radio • 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

  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

  15. Novel Algorithms • 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

  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

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

  19. Future Work: Programming • 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

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