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Random Walk with Restart for Automatic Playlist Continuation and Query-specific Adaptations
- 1. Random Walk with Restart for Automatic Playlist Continuation and Query-specific Adaptations Team Radboud: Timo van Niedek timo@mediadistillery.com Arjen P. de Vries arjen@acm.org RecSys ‘18, Vancouver, Canada
- 3. Random Walk with Restart for Automatic Playlist Continuation and Query-specific Adaptations Team Radboud: Timo van Niedek timo@mediadistillery.com Arjen P. de Vries arjen@acm.org RecSys ‘18, Vancouver, Canada
- 4. Current Challenges of Automatic Playlist Continuation 1)Scalability 2)Playlist purpose 3)Popularity bias
- 5. 1. Scalability • RecSys Challenge: 1M playlists, > 2.3M unique tracks
- 6. Recent work: Pixie1 • Recommender system at Pinterest • Highly scalable: 3+ billion items, 200+ million users 1) Eksombatchai, C., Jindal, P., Liu, J. Z., Liu, Y., Sharma, R., Sugnet, C., ... & Leskovec, J. (2018, April). Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time. In Proceedings of the 2018 World Wide Web Conference on World Wide Web (pp. 1775-1784). International World Wide Web Conferences Steering Committee.
- 7. The Playlist Graph • Neighbor sampling in constant time wrt. number of users & items • Assumption: playlists contain similar tracks (user judgment) • Nearby tracks in graph are similar Timo van Niedek. 2018. Random Walk with Restart for Automatic Playlist Continuation and Query-Specific Adaptations. Master’s thesis. Radboud University, Nijmegen.
- 8. Random Walk with Restart • Query track → Random Playlist → Random Track → … • Restart probability: probability of restarting after every step • Track-level recommendations: Sorted list of visit counts • Only local neighborhood is needed: scalable! Timo van Niedek. 2018. Random Walk with Restart for Automatic Playlist Continuation and Query-Specific Adaptations. Master’s thesis. Radboud University, Nijmegen.
- 9. Multiple Random Walks with Restart • Playlist-level recommendations: Repeat Random Walk with Restart for every track in playlist Timo van Niedek. 2018. Random Walk with Restart for Automatic Playlist Continuation and Query-Specific Adaptations. Master’s thesis. Radboud University, Nijmegen.
- 10. Evaluation • Validation set: sample of the Million Playlist Dataset • 10.000 playlists following structure of challenge set
- 12. 2. Playlist Purpose • Playlist purpose = a set of target characteristics • High quality recommendations match these characteristics • Prefiltering: remove playlists that do not match target characteristics
- 13. Measuring Target Characteristics • Target characteristics can be measured from the query playlist • Playlist Title • Query Track Features
- 14. Prefiltering approaches • Title-based: remove playlists whose titles do not match query playlist • Feature-based: prune graph based on distance between playlist models using features from Spotify API: Acousticness, danceability, count, energy, explicitness, instrumentalness, liveness, speechiness, tempo, valence, release year
- 15. Playlist Model • Estimated from empirical distribution as a histogram
- 16. Degree Pruning • Eksombatchai et al. (2018)1 have shown that removing low- quality edges of popular pins is beneficial • Highly popular songs are often added to playlists where they do not belong • Pruning edges of popular songs reduces random walk drift 1) Eksombatchai, C., Jindal, P., Liu, J. Z., Liu, Y., Sharma, R., Sugnet, C., ... & Leskovec, J. (2018, April). Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time. In Proceedings of the 2018 World Wide Web Conference on World Wide Web (pp. 1775-1784). International World Wide Web Conferences Steering Committee.
- 17. Degree Pruning • The degree of all track nodes t is updated to by removing the lowest-quality edges according to where t = collection of track features
- 19. Results
- 20. 3. Popularity Bias • Major issue in many academic approaches1 1) D. Jannach, I. Kamehkhosh, and G. Bonnin, “Biases in automated music playlist generation: A comparison of next-track recommending techniques,” in Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization. ACM, 2016, pp. 281–285.
- 21. 3. Popularity Bias • Major issue in many academic approaches1 • Our methods reduce popularity bias 1) D. Jannach, I. Kamehkhosh, and G. Bonnin, “Biases in automated music playlist generation: A comparison of next-track recommending techniques,” in Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization. ACM, 2016, pp. 281–285.
- 22. Summary • Scalable to large graphs • Flexibility of integrating evidence • Reduction of popularity bias