poster

Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systems

Authors:

Panagiotis Adamopoulos,

Alexander TuzhilinAuthors Info & Claims

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

Pages 351 - 354

https://doi.org/10.1145/2507157.2507229

Published: 12 October 2013 Publication History

Abstract

This paper proposes a novel method for estimating unknown ratings and recommendation opportunities and illustrates the practical implementation of the proposed approach by presenting a certain variation of the classical k-NN method in neighborhood-based collaborative filtering systems using weighted percentiles. We conduct an empirical study showing that the proposed method outperforms the standard user-based collaborative filtering approach by a wide margin in terms of item prediction accuracy and utility-based ranking metrics across various experimental settings. We also demonstrate that this performance improvement is not achieved at the expense of other popular performance measures, such as catalog coverage and aggregate diversity. The proposed approach can also be applied to other popular methods for rating estimation.

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Cited By

Adamopoulos PTodri V(2024)Consumer Social Connectedness and Persuasiveness of Collaborative-Filtering Recommender Systems: Evidence From an Online-to-Offline Recommendation AppProduction and Operations Management10.1177/10591478241259422Online publication date: 25-Jul-2024
https://doi.org/10.1177/10591478241259422
Sun TYuan ZLi CZhang KXu J(2022)The Value of Personal Data in Internet Commerce: A High-Stake Field Experiment on Data Regulation PolicySSRN Electronic Journal10.2139/ssrn.3962157Online publication date: 2022
https://doi.org/10.2139/ssrn.3962157
Kokkodis M(2021)Dynamic, Multidimensional, and Skillset-Specific Reputation Systems for Online WorkInformation Systems Research10.1287/isre.2020.0972Online publication date: 31-Mar-2021
https://doi.org/10.1287/isre.2020.0972
Show More Cited By

Index Terms

Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systems
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications

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Published In

cover image ACM Conferences

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

October 2013

516 pages

ISBN:9781450324090

DOI:10.1145/2507157

General Chairs:
Qiang Yang
Huawei Noah Ark Research Lab, China
,
Irwin King
Chinese University of Hong Kong, China
,
Qing Li
City University of Hong Kong, China
,
Program Chairs:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne, Switzerland
,
George Karypis
University of Minnesota, USA

Copyright © 2013 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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Publication History

Published: 12 October 2013

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RecSys '13

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SIGCHI

RecSys '13: Seventh ACM Conference on Recommender Systems

October 12 - 16, 2013

Hong Kong, China

Acceptance Rates

RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;

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

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Cited By

Adamopoulos PTodri V(2024)Consumer Social Connectedness and Persuasiveness of Collaborative-Filtering Recommender Systems: Evidence From an Online-to-Offline Recommendation AppProduction and Operations Management10.1177/10591478241259422Online publication date: 25-Jul-2024
https://doi.org/10.1177/10591478241259422
Sun TYuan ZLi CZhang KXu J(2022)The Value of Personal Data in Internet Commerce: A High-Stake Field Experiment on Data Regulation PolicySSRN Electronic Journal10.2139/ssrn.3962157Online publication date: 2022
https://doi.org/10.2139/ssrn.3962157
Kokkodis M(2021)Dynamic, Multidimensional, and Skillset-Specific Reputation Systems for Online WorkInformation Systems Research10.1287/isre.2020.0972Online publication date: 31-Mar-2021
https://doi.org/10.1287/isre.2020.0972
Adamopoulos PTodri VGhose A(2021)Demand Effects of the Internet-of-Things Sales Channel: Evidence from Automating the Purchase ProcessInformation Systems Research10.1287/isre.2020.096232:1(238-267)Online publication date: 1-Mar-2021
https://doi.org/10.1287/isre.2020.0962
Dixit VJain P(2018)An Improved Similarity Measure to Alleviate Sparsity Problem in Context-Aware Recommender SystemsTowards Extensible and Adaptable Methods in Computing10.1007/978-981-13-2348-5_21(281-295)Online publication date: 5-Nov-2018
https://doi.org/10.1007/978-981-13-2348-5_21
Dixit VJain P(2018)Weighted Percentile-Based Context-Aware Recommender SystemApplications of Artificial Intelligence Techniques in Engineering10.1007/978-981-13-1822-1_35(377-388)Online publication date: 19-Sep-2018
https://doi.org/10.1007/978-981-13-1822-1_35
Dobbins CRawassizadeh RMomeni E(2017)Detecting physical activity within lifelogs towards preventing obesity and aiding ambient assisted livingNeurocomputing10.1016/j.neucom.2016.02.088230:C(110-132)Online publication date: 22-Mar-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.02.088
Su HLin XWang CYan BZheng H(2015)Parallel Collaborative Filtering Recommendation Model Based on Two-Phase SimilarityIntelligent Computing Theories and Methodologies10.1007/978-3-319-22180-9_1(1-10)Online publication date: 11-Aug-2015
https://doi.org/10.1007/978-3-319-22180-9_1
Adamopoulos PTuzhilin AKobsa AZhou MEster MKoren Y(2014)On over-specialization and concentration bias of recommendationsProceedings of the 8th ACM Conference on Recommender systems10.1145/2645710.2645752(153-160)Online publication date: 6-Oct-2014
https://dl.acm.org/doi/10.1145/2645710.2645752
Adamopoulos PTuzhilin A(2014)On Unexpectedness in Recommender SystemsACM Transactions on Intelligent Systems and Technology10.1145/25599525:4(1-32)Online publication date: 18-Dec-2014
https://dl.acm.org/doi/10.1145/2559952
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