Automatic Playlist Continuation Menggunakan Hybrid Recommender System

Martin Andersen Linggajaya(1*), Henry Novianus Palit(2), Alvin Nathaniel Tjondrowiguno(3),


(1) Program Studi Informatika
(2) Program Studi Informatika
(3) Program Studi Informatika
(*) Corresponding Author

Abstract


One of the most popular ways to listen to music is using playlists. The playlist feature can be improved by giving track recommendations to be added to certain playlists. To support the development of this recommendation process, ACM and Spotify held the RecSys Challenge 2018 with the task of automatic playlist continuation.

This research is a continuation from [6] that placed 3rd in the RecSys Challenge 2018. The method used consists of 2 phases: candidate selection using a hybrid recommender system called LightFM and ranking using XGBoost. The research gap being developed focuses on one of the calculations for co-occurrence features used in the ranking phase.

The result of this research shows that co-occurrence of 3 tracks does not improve the performance of the model used. The model by [6] achieved scores of 0.5251, 0.5582, and 1.295 for R-precision, NDCG, and recommended song clicks respectively. Meanwhile, the model produced in this research achieved an R-precision of 0.5241, an NDCG of 0.5579, and recommend song clicks of 1.312.


Keywords


hybrid recommender system; LightFM; XGBoost; automatic playlist continuation

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References


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