research-article

A collaborative constraint-based meta-level recommender

Author:

Markus ZankerAuthors Info & Claims

RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems

Pages 139 - 146

https://doi.org/10.1145/1454008.1454032

Published: 23 October 2008 Publication History

Abstract

Recommender Systems (RS) have become popular for their ability to make useful suggestions to online shoppers. Knowledge-based RS represent one branch of these types of applications that employ means-end knowledge to map abstract user requirements to product characteristics. Before setting up such a system, the knowledge has to be acquired from domain experts and formalized using constraints or a comparable representation mechanism. However, the initial acquisition of the knowledge base and its maintenance are effort intensive tasks. Here, we propose a system that learns rule-based preferences from successful interactions in historic transaction data. It is realized as a meta-level hybrid that employs collaborative filtering to derive preferences from a user's nearest neighbors that are processed by a knowledge-based RS to derive recommendations. An evaluation using a commercial dataset showed that this approach outperforms the prediction accuracy of a knowledge base provided by domain experts. In addition, the approach is applicable for supporting domain experts in the maintenance and validation tasks associated with providing personalization knowledge bases.

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Cited By

Popescu APolat-Erdeniz SFelfernig AUta MAtas MLe VPilsl KEnzelsberger MTran T(2021)An overview of machine learning techniques in constraint solvingJournal of Intelligent Information Systems10.1007/s10844-021-00666-5Online publication date: 30-Aug-2021
https://doi.org/10.1007/s10844-021-00666-5
Hrnjica BMusic DSoftic S(2020)Model-Based Recommender SystemsTrends in Cloud-based IoT10.1007/978-3-030-40037-8_8(125-146)Online publication date: 2-Jun-2020
https://doi.org/10.1007/978-3-030-40037-8_8
Erdeniz SFelfernig ASamer RAtas MHung CPapadopoulos G(2019)Matrix factorization based heuristics for constraint-based recommendersProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297441(1655-1662)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297441
Show More Cited By

Index Terms

A collaborative constraint-based meta-level recommender
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
    2. Retrieval models and ranking
  2. Information systems applications
    1. Decision support systems
      1. Expert systems

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Published In

cover image ACM Conferences

RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems

October 2008

348 pages

ISBN:9781605580937

DOI:10.1145/1454008

General Chair:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
,
Program Chairs:
Derek Bridge
University College Cork, Ireland
,
Bamshad Mobasher
DePaul University, USA
,
Francesco Ricci
Free University Bozen-Bolzano, Italy

Copyright © 2008 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: 23 October 2008

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RecSys08: ACM Conference on Recommender Systems

October 23 - 25, 2008

Lausanne, Switzerland

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

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Cited By

Popescu APolat-Erdeniz SFelfernig AUta MAtas MLe VPilsl KEnzelsberger MTran T(2021)An overview of machine learning techniques in constraint solvingJournal of Intelligent Information Systems10.1007/s10844-021-00666-5Online publication date: 30-Aug-2021
https://doi.org/10.1007/s10844-021-00666-5
Hrnjica BMusic DSoftic S(2020)Model-Based Recommender SystemsTrends in Cloud-based IoT10.1007/978-3-030-40037-8_8(125-146)Online publication date: 2-Jun-2020
https://doi.org/10.1007/978-3-030-40037-8_8
Erdeniz SFelfernig ASamer RAtas MHung CPapadopoulos G(2019)Matrix factorization based heuristics for constraint-based recommendersProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297441(1655-1662)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297441
Prakash KAsad FUrolagin S(2019)User and Item Preference Learning for Hybrid Recommendation SystemsSmart Technologies and Innovation for a Sustainable Future10.1007/978-3-030-01659-3_50(447-455)Online publication date: 9-Jan-2019
https://doi.org/10.1007/978-3-030-01659-3_50
Wang CKalra AZhou LBorcea CChen Y(2017)Probabilistic Models for Ad Viewability Prediction on the WebIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.270568829:9(2012-2025)Online publication date: 1-Sep-2017
https://doi.org/10.1109/TKDE.2017.2705688
Gachet Páez Dde Buenaga Rodríguez MPuertas Sánz EVillalba MMuñoz Gil R(2016)Healthy and wellbeing activities’ promotion using a Big Data approachHealth Informatics Journal10.1177/146045821666075424:2(125-135)Online publication date: 4-Aug-2016
https://doi.org/10.1177/1460458216660754
Felfernig AFriedrich GJannach DZanker M(2015)Constraint-Based Recommender SystemsRecommender Systems Handbook10.1007/978-1-4899-7637-6_5(161-190)Online publication date: 2015
https://doi.org/10.1007/978-1-4899-7637-6_5
Rapti EKarageorgos ANtalos G(2014)Adaptive Constraint and Rule-Based Product Bundling in Enterprise NetworksProceedings of the 2014 IEEE 23rd International WETICE Conference10.1109/WETICE.2014.15(15-20)Online publication date: 23-Jun-2014
https://dl.acm.org/doi/10.1109/WETICE.2014.15
Karim J(2014)Hybrid system for personalized recommendations2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS)10.1109/RCIS.2014.6861080(1-6)Online publication date: May-2014
https://doi.org/10.1109/RCIS.2014.6861080
Ghorai KSaha SBakshi AMahanti ARay P(2013)An mHealth Recommender for Smoking Cessation Using Case Based ReasoningProceedings of the 2013 46th Hawaii International Conference on System Sciences10.1109/HICSS.2013.89(2695-2704)Online publication date: 7-Jan-2013
https://dl.acm.org/doi/10.1109/HICSS.2013.89
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