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Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems

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Anto PedoneAuthors Info & Claims

RecSys '09: Proceedings of the third ACM conference on Recommender systems

Pages 265 - 268

https://doi.org/10.1145/1639714.1639764

Published: 23 October 2009 Publication History

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Abstract

Recently, methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. Although some of these methods have been studied independently, no prior research compared the performance of these methods to determine which of them is better than the others. This paper focuses on comparing the pre-filtering and the post-filtering approaches and identifying which method dominates the other and under which circumstances. Since there are no clear winners in this comparison, we propose an alternative more effective method of selecting the winners in the pre- vs. the post-filtering comparison. This strategy provides analysts and companies with a practical suggestion on how to pick a good pre- or post-filtering approach in an effective manner to improve performance of a context-aware recommender system.

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Cited By

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Wang LMa CWu XQiu ZZheng YChen XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Causally Debiased Time-aware RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645400(3331-3342)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645400
Antal LIantovics L(2024)Advanced Data Analysis for Machine Learning-powered Recommender SystemsProcedia Computer Science10.1016/j.procs.2024.09.170246(3957-3966)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.09.170
Rana ASanner SBouadjenek MDi Carlantonio RFarmaner G(2023)User Experience and the Role of Personalization in Critiquing-Based Conversational RecommendationACM Transactions on the Web10.1145/359749918:4(1-21)Online publication date: 18-May-2023
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Index Terms

Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems
1. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

RecSys '09: Proceedings of the third ACM conference on Recommender systems

October 2009

442 pages

ISBN:9781605584355

DOI:10.1145/1639714

General Chairs:
Lawrence Bergman
IBM Research, NY, USA
,
Alex Tuzhilin
New York University, NY, USA
,
Program Chairs:
Robin Burke
DePaul University, USA
,
Alexander Felfernig
Graz University of Technology, Austria
,
Lars Schmidt-Thieme
University of Hildesheim, Germany

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2009

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Short-paper

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RecSys '09

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SIGCHI

RecSys '09: Third ACM Conference on Recommender Systems

October 23 - 25, 2009

New York, New York, USA

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

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Wang LMa CWu XQiu ZZheng YChen XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Causally Debiased Time-aware RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645400(3331-3342)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645400
Antal LIantovics L(2024)Advanced Data Analysis for Machine Learning-powered Recommender SystemsProcedia Computer Science10.1016/j.procs.2024.09.170246(3957-3966)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.09.170
Rana ASanner SBouadjenek MDi Carlantonio RFarmaner G(2023)User Experience and the Role of Personalization in Critiquing-Based Conversational RecommendationACM Transactions on the Web10.1145/359749918:4(1-21)Online publication date: 18-May-2023
https://dl.acm.org/doi/10.1145/3597499
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Sattar ABacciu D(2022)Graph Neural Network for Context-Aware RecommendationNeural Processing Letters10.1007/s11063-022-10917-355:5(5357-5376)Online publication date: 20-Jun-2022
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Velankar MKulkarni P(2022)Music Recommendation Systems: Overview and ChallengesAdvances in Speech and Music Technology10.1007/978-3-031-18444-4_3(51-69)Online publication date: 23-Sep-2022
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Vultureanu-Albisi ABadica C(2021)Explainable Collaborative Filtering Recommendations Enriched with Contextual Information2021 25th International Conference on System Theory, Control and Computing (ICSTCC)10.1109/ICSTCC52150.2021.9607106(701-706)Online publication date: 20-Oct-2021
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Dadoun ADefoin-Platel MFiig TLandra CTroncy R(2021)How recommender systems can transform airline offer construction and retailingJournal of Revenue and Pricing Management10.1057/s41272-021-00313-220:3(301-315)Online publication date: 20-Mar-2021
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Distributional semantic pre-filtering in context-aware recommender systems

Characterizing context-aware recommender systems

Comparing Pre-filtering and Post-filtering Approach in a Collaborative Contextual Recommender System: An Application to E-Commerce