Article

Addressing uncertainty in implicit preferences

Authors:

Sandra Clara Gadanho,

Nicolas LhuillierAuthors Info & Claims

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

Pages 97 - 104

https://doi.org/10.1145/1297231.1297248

Published: 19 October 2007 Publication History

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Abstract

The increasing amount of content available via digital television has made TV program recommenders valuable tools. In order to provide personalized recommendations, recommender systems need to collect information about user preferences. Since users are reluctant to invest much time in explicitly expressing their interests, preferences often need to be implicitly inferred through data gathered by monitoring user behavior. Which is, alas, less reliable.

This article addresses the problem of learning TV preferences based on tracking the programs users have watched, whilst dealing with the varying degrees of reliability in such information. Three approaches to the problem are discussed: use all information equally; weight information by its reliability or simply discard the most unreliable information.

Experimental results for these three approaches are presented and compared using a content-based filtering recommender built on a Naïve Bayes classifier.

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Cited By

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Ogunseyi TAvoussoukpo CJiang Y(2023)A systematic review of privacy techniques in recommendation systemsInternational Journal of Information Security10.1007/s10207-023-00710-122:6(1651-1664)Online publication date: 5-Jun-2023
https://doi.org/10.1007/s10207-023-00710-1
Singhal SPathak NSingh B(2022)Parameter Boosted Approach to Collaborative Filtering Based Recommender SystemAdvances in Micro-Electronics, Embedded Systems and IoT10.1007/978-981-16-8550-7_44(457-464)Online publication date: 23-Apr-2022
https://doi.org/10.1007/978-981-16-8550-7_44
Trichur Narayanan R(2021)Recommender System: Personalizing User Experience or Scientifically Deceiving Users?Proceedings of the 2021 5th International Conference on Information System and Data Mining10.1145/3471287.3471303(138-144)Online publication date: 27-May-2021
https://dl.acm.org/doi/10.1145/3471287.3471303
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Index Terms

Addressing uncertainty in implicit preferences
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications

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Published In

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

October 2007

222 pages

ISBN:9781595937308

DOI:10.1145/1297231

General Chair:
Joseph A. Konstan
University of Minnesota, USA
,
Program Chairs:
John Riedl
University of Minnesota, USA
,
Barry Smyth
University College, Dublin, Ireland

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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Association for Computing Machinery

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Published: 19 October 2007

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RecSys07: ACM Conference on Recommender Systems

October 19 - 20, 2007

MN, Minneapolis, USA

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

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View all

Ogunseyi TAvoussoukpo CJiang Y(2023)A systematic review of privacy techniques in recommendation systemsInternational Journal of Information Security10.1007/s10207-023-00710-122:6(1651-1664)Online publication date: 5-Jun-2023
https://doi.org/10.1007/s10207-023-00710-1
Singhal SPathak NSingh B(2022)Parameter Boosted Approach to Collaborative Filtering Based Recommender SystemAdvances in Micro-Electronics, Embedded Systems and IoT10.1007/978-981-16-8550-7_44(457-464)Online publication date: 23-Apr-2022
https://doi.org/10.1007/978-981-16-8550-7_44
Trichur Narayanan R(2021)Recommender System: Personalizing User Experience or Scientifically Deceiving Users?Proceedings of the 2021 5th International Conference on Information System and Data Mining10.1145/3471287.3471303(138-144)Online publication date: 27-May-2021
https://dl.acm.org/doi/10.1145/3471287.3471303
Shu YLi B(2021)Surface Defect Detection and Recognition Method for Multi-Scale Commutator Based on Deep Transfer LearningArabian Journal for Science and Engineering10.1007/s13369-021-05963-3Online publication date: 30-Jul-2021
https://doi.org/10.1007/s13369-021-05963-3
Jain AVishwakarma SJain P(2020)An Efficient Collaborative Recommender System for Removing Sparsity ProblemICT Analysis and Applications10.1007/978-981-15-0630-7_14(131-141)Online publication date: 4-Feb-2020
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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
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Jannach DLerche LZanker M(2018)Recommending Based on Implicit FeedbackSocial Information Access10.1007/978-3-319-90092-6_14(510-569)Online publication date: 3-May-2018
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Jin YJunhua GSuqi ZJian Z(2017)Spark-based Distributed Multi-features Hybrid IPTV Viewing Implicit Feedback Scoring ModelProcedia Computer Science10.1016/j.procs.2017.06.046111:C(441-447)Online publication date: 1-Sep-2017
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Isinkaye FFolajimi YOjokoh B(2015)Recommendation systems: Principles, methods and evaluationEgyptian Informatics Journal10.1016/j.eij.2015.06.00516:3(261-273)Online publication date: Nov-2015
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Abstract

References

Cited By

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Recommendations

Novelty Learning via Collaborative Proximity Filtering

Implicit Rating Methods Based on Interest Preferences of Categories for Micro-Video Recommendation

Personalized recommendations on books for K-12 readers

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