research-article

Action prediction models for recommender systems based on collaborative filtering and sequence mining hybridization

Authors:

Tural Gurbanov and

Francesco RicciAuthors Info & Claims

SAC '17: Proceedings of the Symposium on Applied Computing

April 2017

Pages 1655 - 1661

https://doi.org/10.1145/3019612.3019759

Published: 03 April 2017 Publication History

Abstract

Many recommender systems collect online users' activity and infer from it users' preferences. They record user actions of various types (e.g. clicks, views), and predict unknown, possibly future, interactions between users and items, mostly using Collaborative Filtering (CF) or Sequence Mining (SM) techniques. While both techniques have their advantages, in this paper, we show that improved prediction accuracy can be achieved by hybridizing them. The proposed hybrid model uses first an SM model to augment an existing actions' data set and then uses collaborative filtering in the final prediction step. The empirical evaluation, which was conducted on a large real-world dataset, showed that the proposed hybrid model outperforms both stand-alone SM and CF.

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Cited By

Ricci F(2022)Recommender Systems in TourismHandbook of e-Tourism10.1007/978-3-030-48652-5_26(457-474)Online publication date: 2-Sep-2022
https://doi.org/10.1007/978-3-030-48652-5_26
Yang T(2021)Item Recommendation Based on Monotonous Behavior ChainsData Science10.1007/978-981-16-5940-9_2(17-31)Online publication date: 10-Sep-2021
https://doi.org/10.1007/978-981-16-5940-9_2
Kolahkaj MHarounabadi ANikravanshalmani AChinipardaz R(2020)A hybrid context-aware approach for e-tourism package recommendation based on asymmetric similarity measurement and sequential pattern miningElectronic Commerce Research and Applications10.1016/j.elerap.2020.10097842(100978)Online publication date: Jul-2020
https://doi.org/10.1016/j.elerap.2020.100978
Show More Cited By

Index Terms

Action prediction models for recommender systems based on collaborative filtering and sequence mining hybridization
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

SAC '17: Proceedings of the Symposium on Applied Computing

April 2017

2004 pages

ISBN:9781450344869

DOI:10.1145/3019612

Conference Chair:
Sung Y. Shin
South Dakota State University
,
Program Chairs:
Dongwan Shin
New Mexico Tech
,
Maria Lencastre
University of Pernambuco, Brazil

Copyright © 2017 ACM.

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SIGAPP: ACM Special Interest Group on Applied Computing

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Publication History

Published: 03 April 2017

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SAC 2017

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SAC 2017: Symposium on Applied Computing

April 3 - 7, 2017

Marrakech, Morocco

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Ricci F(2022)Recommender Systems in TourismHandbook of e-Tourism10.1007/978-3-030-48652-5_26(457-474)Online publication date: 2-Sep-2022
https://doi.org/10.1007/978-3-030-48652-5_26
Yang T(2021)Item Recommendation Based on Monotonous Behavior ChainsData Science10.1007/978-981-16-5940-9_2(17-31)Online publication date: 10-Sep-2021
https://doi.org/10.1007/978-981-16-5940-9_2
Kolahkaj MHarounabadi ANikravanshalmani AChinipardaz R(2020)A hybrid context-aware approach for e-tourism package recommendation based on asymmetric similarity measurement and sequential pattern miningElectronic Commerce Research and Applications10.1016/j.elerap.2020.10097842(100978)Online publication date: Jul-2020
https://doi.org/10.1016/j.elerap.2020.100978
Elahi MQi L(2020)Fashion Recommender Systems in Cold StartFashion Recommender Systems10.1007/978-3-030-55218-3_1(3-21)Online publication date: 5-Nov-2020
https://doi.org/10.1007/978-3-030-55218-3_1
Ricci F(2020)Recommender Systems in TourismHandbook of e-Tourism10.1007/978-3-030-05324-6_26-1(1-18)Online publication date: 31-Jan-2020
https://doi.org/10.1007/978-3-030-05324-6_26-1
Wan MMcAuley JPera SEkstrand MAmatriain XO'Donovan J(2018)Item recommendation on monotonic behavior chainsProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240369(86-94)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3240369
Shi CLiu JZhang YHu BLiu SYu P(2018)MFPRACM Transactions on Social Computing10.1145/32163681:2(1-22)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3216368
Dai QHong XCai JLiu YZhao HLuo JLin ZChen S(2018)Deep Learning Based Recommendation Algorithm in Online Medical PlatformAdvances in Brain Inspired Cognitive Systems10.1007/978-3-030-00563-4_4(34-43)Online publication date: 6-Oct-2018
https://doi.org/10.1007/978-3-030-00563-4_4
Wei SLi K(2017)TF: A Novel Filtering Approach to Find Temporal Frequent Itemsets in Recommender Systems2017 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI.2017.258(1477-1482)Online publication date: Dec-2017
https://doi.org/10.1109/CSCI.2017.258

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