Cold-start playlist recommendation with multitask learning
| Autor: | Aditya Krishna Menon, Cheng Soon Ong, Dawei Chen |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Information retrieval Computer science InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g. HCI) InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL Multi-task learning Bipartite ranking Machine Learning (stat.ML) Machine Learning (cs.LG) Computer Science - Information Retrieval 68T05 Set (abstract data type) Binary classification Cold start Statistics - Machine Learning Cold start recommendation Equivalence (measure theory) Information Retrieval (cs.IR) |
| DOI: | 10.7287/peerj.preprints.27383v2 |
| Popis: | Playlist recommendation involves producing a set of songs that a user might enjoy. We investigate this problem in three cold-start scenarios: (i) cold playlists, where we recommend songs to form new personalised playlists for an existing user; (ii) cold users, where we recommend songs to form new playlists for a new user; and (iii) cold songs, where we recommend newly released songs to extend users' existing playlists. We propose a flexible multitask learning method to deal with all three settings. The method learns from user-curated playlists, and encourages songs in a playlist to be ranked higher than those that are not by minimising a bipartite ranking loss. Inspired by an equivalence between bipartite ranking and binary classification, we show how one can efficiently approximate an optimal solution of the multitask learning objective by minimising a classification loss. Empirical results on two real playlist datasets show the proposed approach has good performance for cold-start playlist recommendation. 15 pages |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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