research-article

A hybrid two-stage recommender system for automatic playlist continuation

Authors:

Vasiliy Rubtsov,

Mikhail Kamenshchikov,

Vasiliy Leksin,

Dmitry I. IgnatovAuthors Info & Claims

RecSys Challenge '18: Proceedings of the ACM Recommender Systems Challenge 2018

Article No.: 16, Pages 1 - 4

https://doi.org/10.1145/3267471.3267488

Published: 02 October 2018 Publication History

Abstract

In this paper, we provide the solution for RecSys Challenge 2018 by our Avito team, which obtained the 3rd place in main track. The goal of the competition was to recommend music tracks for automatic playlist continuation. As a part of this challenge, Spotify released a large public dataset, which allowed us to train a rather complex algorithm. Our approach consists of two stages: collaborative filtering for candidate selection and gradient boosting for final prediction. The combination of these two models performed well with the playlist and track metadata given.

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Cited By

Kalamaras ISánchez-Corcuera RCasado-Mansilla DTsolakis AGómez-Carmona OKrinidis SBorges CTzovaras DLópez-de-Ipiña D(2024)A cascading model for nudging employees towards energy-efficient behaviour in tertiary buildingsPLOS ONE10.1371/journal.pone.030321419:5(e0303214)Online publication date: 16-May-2024
https://doi.org/10.1371/journal.pone.0303214
Gabbolini GBridge D(2024)Surveying More Than Two Decades of Music Information Retrieval Research on PlaylistsACM Transactions on Intelligent Systems and Technology10.1145/368839815:6(1-68)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3688398
Yürekli AKaleli CBilge A(2021)Alleviating the cold-start playlist continuation in music recommendation using latent semantic indexingInternational Journal of Multimedia Information Retrieval10.1007/s13735-021-00214-510:3(185-198)Online publication date: 3-Sep-2021
https://doi.org/10.1007/s13735-021-00214-5
Show More Cited By

Index Terms

A hybrid two-stage recommender system for automatic playlist continuation
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods
        Boosting
    2. Machine learning approaches
      1. Factorization methods
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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RecSys Challenge '18: Proceedings of the ACM Recommender Systems Challenge 2018

The ACM RecSys Challenge 2018 focuses on music recommendation in the context of automatic playlist continuation. In this paper, we describe our approach to the problem and the final hybrid system that was submitted to the challenge by our team ...

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Published In

cover image ACM Other conferences

RecSys Challenge '18: Proceedings of the ACM Recommender Systems Challenge 2018

October 2018

96 pages

ISBN:9781450365864

DOI:10.1145/3267471

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 October 2018

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RecSys Challenge '18

RecSys Challenge '18: Proceedings of the ACM Recommender Systems Challenge 2018

October 2, 2018

BC, Vancouver, Canada

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Overall Acceptance Rate 11 of 15 submissions, 73%

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Cited By

Kalamaras ISánchez-Corcuera RCasado-Mansilla DTsolakis AGómez-Carmona OKrinidis SBorges CTzovaras DLópez-de-Ipiña D(2024)A cascading model for nudging employees towards energy-efficient behaviour in tertiary buildingsPLOS ONE10.1371/journal.pone.030321419:5(e0303214)Online publication date: 16-May-2024
https://doi.org/10.1371/journal.pone.0303214
Gabbolini GBridge D(2024)Surveying More Than Two Decades of Music Information Retrieval Research on PlaylistsACM Transactions on Intelligent Systems and Technology10.1145/368839815:6(1-68)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3688398
Yürekli AKaleli CBilge A(2021)Alleviating the cold-start playlist continuation in music recommendation using latent semantic indexingInternational Journal of Multimedia Information Retrieval10.1007/s13735-021-00214-510:3(185-198)Online publication date: 3-Sep-2021
https://doi.org/10.1007/s13735-021-00214-5
Yürekli ABilge AKaleli C(2021)Exploring playlist titles for cold-start music recommendation: an effectiveness analysisJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-02777-312:11(10125-10144)Online publication date: 3-Jan-2021
https://doi.org/10.1007/s12652-020-02777-3
Jannach Dde Souza P. Moreira GOldridge E(2020)Why Are Deep Learning Models Not Consistently Winning Recommender Systems Competitions Yet?Proceedings of the Recommender Systems Challenge 202010.1145/3415959.3416001(44-49)Online publication date: 26-Sep-2020
https://dl.acm.org/doi/10.1145/3415959.3416001
Sánchez-Corcuera RCasado-Mansilla DBorges CLópez-de-Ipiña D(2020)Persuasion-based recommender system ensambling matrix factorisation and active learning modelsPersonal and Ubiquitous Computing10.1007/s00779-020-01382-728:1(247-257)Online publication date: 12-Mar-2020
https://doi.org/10.1007/s00779-020-01382-7
Sánchez Rodríguez JWu JKhandwawala M(2020)Two-Stage Session-Based Recommendations with Candidate Rank EmbeddingsFashion Recommender Systems10.1007/978-3-030-55218-3_3(49-66)Online publication date: 5-Nov-2020
https://doi.org/10.1007/978-3-030-55218-3_3
Zamani HSchedl MLamere PChen C(2019)An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist ContinuationACM Transactions on Intelligent Systems and Technology10.1145/334425710:5(1-21)Online publication date: 18-Sep-2019
https://dl.acm.org/doi/10.1145/3344257
Sanchez-Corcuera RCasado-Mansilla DBorges CLopez-De-Ipina D(2019)Persuade Me!: A User-Based Recommendation System Approach2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00310(1740-1745)Online publication date: Aug-2019
https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00310

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