short-paper

Predictability limits in session-based next item recommendation

Author:

Priit JärvAuthors Info & Claims

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

Pages 146 - 150

https://doi.org/10.1145/3298689.3346990

Published: 10 September 2019 Publication History

Abstract

Session-based recommendations are based on the user's recent actions, for example, the items they have viewed during the current browsing session or the sightseeing places they have just visited. Closely related is sequence-aware recommendation, where the choice of the next item should follow from the sequence of previous actions.

We study seven benchmarks for session-based recommendation, covering retail, music and news domains to investigate how accurately user behavior can be predicted from the session histories. We measure the entropy rate of the data and estimate the limit of predictability to be between 44% and 73% in the included datasets.

We establish some algorithm-specific limits on prediction accuracy for Markov chains, association rules and k-nearest neighbors methods. With most of the analyzed methods, the algorithm design limits their performance with sparse training data. The session based k-nearest neighbors are least restricted in comparison and have room for improvement across all of the analyzed datasets.

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Cited By

Xu EZhao KYu ZZhang YGuo BYao L(2024)Limits of predictability in top-N recommendationInformation Processing & Management10.1016/j.ipm.2024.10373161:4(103731)Online publication date: Jul-2024
https://doi.org/10.1016/j.ipm.2024.103731
Bezbochina AStavinova EKovantsev AChunaev P(2023)Enhancing Predictability Assessment: An Overview and Analysis of Predictability Measures for Time Series and Network LinksEntropy10.3390/e2511154225:11(1542)Online publication date: 15-Nov-2023
https://doi.org/10.3390/e25111542
Xu EYu ZLi NCui HYao LGuo B(2022)Quantifying predictability of sequential recommendation via logical constraintsFrontiers of Computer Science10.1007/s11704-022-2223-117:5Online publication date: 24-Dec-2022
https://doi.org/10.1007/s11704-022-2223-1
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Index Terms

Predictability limits in session-based next item recommendation
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Test collections
    2. Retrieval tasks and goals
      1. Recommender systems
2. Mathematics of computing
  1. Information theory

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cover image ACM Other conferences

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

September 2019

635 pages

ISBN:9781450362436

DOI:10.1145/3298689

General Chairs:
Toine Bogers
Aalborg University Copenhagen, Denmark
,
Alan Said
University of Gothenburg, Sweden
,
Program Chairs:
Peter Brusilovsky
University of Pittsburgh
,
Domonkos Tikk
Gravity R&D, Hungary

Copyright © 2019 ACM.

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Publication History

Published: 10 September 2019

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RecSys '19

RecSys '19: Thirteenth ACM Conference on Recommender Systems

September 16 - 20, 2019

Copenhagen, Denmark

Acceptance Rates

RecSys '19 Paper Acceptance Rate 36 of 189 submissions, 19%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Xu EZhao KYu ZZhang YGuo BYao L(2024)Limits of predictability in top-N recommendationInformation Processing & Management10.1016/j.ipm.2024.10373161:4(103731)Online publication date: Jul-2024
https://doi.org/10.1016/j.ipm.2024.103731
Bezbochina AStavinova EKovantsev AChunaev P(2023)Enhancing Predictability Assessment: An Overview and Analysis of Predictability Measures for Time Series and Network LinksEntropy10.3390/e2511154225:11(1542)Online publication date: 15-Nov-2023
https://doi.org/10.3390/e25111542
Xu EYu ZLi NCui HYao LGuo B(2022)Quantifying predictability of sequential recommendation via logical constraintsFrontiers of Computer Science10.1007/s11704-022-2223-117:5Online publication date: 24-Dec-2022
https://doi.org/10.1007/s11704-022-2223-1
Bezbochina AStavinova EKovantsev AChunaev P(2022)Dynamic Classification of Bank Clients by the Predictability of Their Transactional BehaviorComputational Science – ICCS 202210.1007/978-3-031-08751-6_36(502-515)Online publication date: 15-Jun-2022
https://doi.org/10.1007/978-3-031-08751-6_36
Wang SCao LWang YSheng QOrgun MLian D(2021)A Survey on Session-based Recommender SystemsACM Computing Surveys10.1145/346540154:7(1-38)Online publication date: 18-Jul-2021
https://dl.acm.org/doi/10.1145/3465401
Thaipisutikul TShih T(2021)A novel context-aware recommender system based on a deep sequential learning approach (CReS)Neural Computing and Applications10.1007/s00521-020-05640-w33:17(11067-11090)Online publication date: 3-Jan-2021
https://dl.acm.org/doi/10.1007/s00521-020-05640-w
Yuan ZLiu HLiu YZhang DYi FZhu NXiong HHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Spatio-Temporal Dual Graph Attention Network for Query-POI MatchingProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401159(629-638)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401159

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