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Article

Combining collaborative filtering with personal agents for better recommendations

Authors:

Nathaniel Good,

J. Ben Schafer,

Joseph A. Konstan,

John RiedlAuthors Info & Claims

AAAI '99/IAAI '99: Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence

Pages 439 - 446

Published: 18 July 1999 Publication History

Abstract

Information filtering agents and collaborative filtering both attempt to alleviate information overload by identifying which items a user will find worthwhile. Information filtering (IF) focuses on the analysis of item content and the development of a personal user interest profile. Collaborative filtering (CF) focuses on identification of other users with similar tastes and the use of their opinions to recommend items. Each technique has advantages and limitations that suggest that the two could be beneficially combined.This paper shows that a CF framework can be used to combine personal IF agents and the opinions of a community of users to produce better recommendations than either agents or users can produce alone. It also shows that using CF to create a personal combination of a set of agents produces better results than either individual agents or other combination mechanisms. One key implication of these results is that users can avoid having to select among agents; they can use them all and let the CF framework select the best ones for them.

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Cited By

Pavlidis G(2019)On the End-to-End Development of a Cultural Tourism RecommenderInternational Journal of Computational Methods in Heritage Science10.4018/IJCMHS.20190701053:2(73-90)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.4018/IJCMHS.2019070105
Tang MXie HTang T(2018)Combining wAMAN and Matrix Factorization to Optimize One-Class Collaborative Filtering and Its Application in an Emotion-Aware Movie Recommendation SystemProceedings of the 3rd International Conference on Big Data and Computing10.1145/3220199.3220202(108-114)Online publication date: 28-Apr-2018
https://dl.acm.org/doi/10.1145/3220199.3220202
Shen ZShu JXue W(2018)Preferred search over encrypted dataFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-016-6244-512:3(593-607)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11704-016-6244-5
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Index Terms

Combining collaborative filtering with personal agents for better recommendations
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image Guide Proceedings

AAAI '99/IAAI '99: Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence

July 1999

998 pages

ISBN:0262511061

Chairmen:
Jim Hendler
Univ. of Maryland, College Park; and DARP/ISO
,
Devika Subramanian
Rice Univ., Houston, TX
,
Ramasamy Uthurusamy
General Motors Research
,
Barbara Hayes-Roth
Extempo Systems, Inc.

Sponsors

AAAI: Am Assoc for Artifical Intelligence

Publisher

American Association for Artificial Intelligence

United States

Publication History

Published: 18 July 1999

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Cited By

Pavlidis G(2019)On the End-to-End Development of a Cultural Tourism RecommenderInternational Journal of Computational Methods in Heritage Science10.4018/IJCMHS.20190701053:2(73-90)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.4018/IJCMHS.2019070105
Tang MXie HTang T(2018)Combining wAMAN and Matrix Factorization to Optimize One-Class Collaborative Filtering and Its Application in an Emotion-Aware Movie Recommendation SystemProceedings of the 3rd International Conference on Big Data and Computing10.1145/3220199.3220202(108-114)Online publication date: 28-Apr-2018
https://dl.acm.org/doi/10.1145/3220199.3220202
Shen ZShu JXue W(2018)Preferred search over encrypted dataFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-016-6244-512:3(593-607)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11704-016-6244-5
Yue ZJiang YPan DLuo ZTheodoropoulos G(2017)An End-to-end Tag-based Recommendation System for Verbal Reasoning QuestionsProceedings of the 10th EAI International Conference on Simulation Tools and Techniques10.1145/3173519.3173530(131-135)Online publication date: 11-Sep-2017
https://dl.acm.org/doi/10.1145/3173519.3173530
Mutlu BVeas ETrattner C(2016)VizRecACM Transactions on Interactive Intelligent Systems10.1145/29839236:4(1-39)Online publication date: 7-Nov-2016
https://dl.acm.org/doi/10.1145/2983923
Beel JGipp BLanger SBreitinger C(2016)Research-paper recommender systemsInternational Journal on Digital Libraries10.1007/s00799-015-0156-017:4(305-338)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1007/s00799-015-0156-0
Toraman CCan F(2015)A front-page news-selection algorithm based on topic modelling using raw textJournal of Information Science10.1177/016555151558906941:5(676-685)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1177/0165551515589069
Zhao XWang JAllan JCroft Bde Vries AZhai C(2015)A Theoretical Analysis of Two-Stage Recommendation for Cold-Start Collaborative FilteringProceedings of the 2015 International Conference on The Theory of Information Retrieval10.1145/2808194.2809459(71-80)Online publication date: 27-Sep-2015
https://dl.acm.org/doi/10.1145/2808194.2809459
Martinez-Cruz CPorcel CBernabé-Moreno JHerrera-Viedma E(2015)A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modelingInformation Sciences: an International Journal10.1016/j.ins.2015.03.013311:C(102-118)Online publication date: 1-Aug-2015
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Tejeda-Lorente APorcel CBernabé-Moreno JHerrera-Viedma E(2015)REFOREApplied Soft Computing10.1016/j.asoc.2015.02.02430:C(778-791)Online publication date: 1-May-2015
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