[NUGU CONFERENCE 2019] 트랙 A-3 : NUGU 개인화 음악 추천 기술 소개
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발표자 : SK텔레콤 사용자이해기술 Cell 신동훈
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[NUGU CONFERENCE 2019] 트랙 A-3 : NUGU 개인화 음악 추천 기술 소개
- 1. NUGU 개인화 음악 추천 기술 소개 SK 텔레콤 사용자이해기술 Cell 신동훈
- 2. Music Recommendation @ NUGU
- 3. Contents 1. Challenges in Music Recommendation 2. System Overview 3. User Representation 4. Context Awareness 5. Realtime Dynamic Re-Ranking 6. Future Plan
- 4. CHALLENGES
- 5. Challenges in Music Recommendation Duration Usually less than 5 min. Scalability Over 30 million target items Consumption Behavior Sequential, Passively consumed Emotion Affect musical preference Listening Context Time, Place, Occasion
- 6. vs • Voice Interface • Hands-free, Eyes-free • Multiple User • Multiple Preference • Multiple Usability • T.P.O Challenges in NUGU Music Recommendation
- 7. 3 Contents Provider* 30 million tracks 1.2 million Active Target User Music Recommendation @ NUGU * FLO, Melon, Bugs
- 9. Candidate Logic #1 Candidate Logic #2 Candidate Logic #N … Preference Re-ranking T.P.O Re-ranking Realtime Re-ranking 1st-stage 2nd-stage Itemmetadata UserLog Track Representation Model Track T.P.O Model M.P. User Model K.P.I Optimization Model User Profile (Demographic, Genre Pref., Artist Pref., Track Pref., etc.) ContentProvider Recommendation User Feedback Two-stage Cascaded Hybrid Architecture High Recall High Precision KPI Optimization
- 10. Candidate Generation (1st stage) Collaborative Filtering based Contents based Profile based Heuristic • BPR • Item KNN • WRMF • Neural CF • … • Audio Signal • Session Song2Vec • TPO Rec. • … • M.P UM ANN • Meta. Pref. • … • Popularity • Recency / Frequency • … • 4 main candidate generation logics • 10+ logic variations • 100 candidate each • Recall focused optimization
- 11. Re-ranking (2nd stage) Candidate Track List Final Recommendation User Preference Seasonality Short-term Interest Static Re-Ranking Layer Playtime focused LTR T.P.O Model RL-based Dynamic Re-ranking ~1000s 50
- 12. First-time User Light User Active User Definition New User, No Interaction Few Interaction No Consume Pattern Active Interaction Pattern Captured Approach User Profile Popularity Acoustic CBF, User Profile , CF (Hybridization) N/A Exploration Exploitation Cold Starter Handling
- 15. 0.6 [ 0.2918, 0.1129, -0.0071, 0.0827, … ] 0.3 [ -0.1850, 0.0314, -0.0631, 0.0503, … ] 0.1 [ 0.7327, 0.0514, 0.0838, -0.1591, … ] Multi-Preference User Model Preprocessing SessionRecovery Contents Preference #1 Contents Preference #2 Contents Preference #3 Outlier Outlier Song Representation User Representation UserLogItemmetadata ContentProviderNUGU Song2Vec Similar Track
- 16. User Description & Segmentation User Description Implicit Intent Explicit Intent Negative Feedback Estimator Demography
- 17. User Description & Segmentation User Segments
- 19. Music Seasonality - Time, Place, Occasion 봄, 벚꽃 이별 가을
- 20. Right Song, Right User, Right Time 가을에 어울리는 노래 여름에 어울리는 노래
- 24. Realtime User Feedback Loop • Better Recommendation • Exploration & Exploitation a Search Space • Multi-Armed Bandit System • Gradually drives more traffic towards the better items (i.e. Song) • Faster and more Efficient process to get the best possible items
- 25. UserLogItemmetadata ContentProviderNUGU User Preference Model User Segmentation Seg. #1 Simulation Env. Real Env. Personalized Traffic Control Recommendation & User Feedback Realtime User Feedback Loop
- 26. Future Works • Realtime feedback based track continuation • T.P.O based personalized playlist generation “노래 틀어줘” 벚꽃 비운동 출근
- 27. 감사합니다