research-article

Content-Independent Task-Focused Recommendation

Authors:

Jonathan L. Herlocker,

Joseph A. KonstanAuthors Info & Claims

IEEE Internet Computing, Volume 5, Issue 6

Pages 40 - 47

https://doi.org/10.1109/4236.968830

Published: 01 November 2001 Publication History

Abstract

Recommender systems, also known as collaborative filtering systems, predict a user's current interest in unseen items based on ratings or recommendations from other people. Users are typically asked to rate items (such as movies) that they have already seen or experienced. The recommender system then matches each user up with other people, known as neighbors, who have given similar ratings and recommends items that the user's neighbors have rated highly. Such recommender systems assume that a user's interest is based solely on historical ratings data and is independent of the user's current task. In reality, the user's current context or task greatly affects the value of a recommendation. This article presents a new approach that provides task-focused recommendation that is independent of the type of content being recommended. The authors have implemented the proposed system on a movie recommendation site and validated it with empirical results from user studies.

References

[1]

J.L. Herlocker, Understanding and Improving Automated Collaborative Filtering Systems, doctoral dissertation, Dept. of Computer Science and Engineering, Univ. of Minnesota, Minneapolis, Minn., 2000.

[2]

Special issue on personalization, M.D. Mulvenna, SS. Anand, A.G. Büchner;, and D. Riecken eds., Comm. ACM, vol. 43, no. 8, Aug. 2000.

[3]

Badrul Sarwar, George Karypis, Joseph Konstan, John Riedl, Analysis of recommendation algorithms for e-commerce, Proceedings of the 2nd ACM conference on Electronic commerce, p.158-167, October 17-20, 2000, Minneapolis, Minnesota, United States

Digital Library

[4]

, WebKDD 2001 Workshop, held in conjunction with ACM Int'l Conf. on Knowledge Discovery and Data Mining, 2001; accepted workshop papers available online at

[5]

P. Resnick et al. "GroupLens: An open architecture for Collaborative Filtering of netnews," Proc. 1994 Conference on Computer Supported Cooperative Work (CSCW), ACM Press, New York, 1994, pp. 175-186.

[6]

W. Hill et al., "Recommending and Evaluating Choices in a Virtual Community of Use," Proc. ACM CHI95 Conf. Human Factors in Computing Systems, ACM Press, New York, 1995, pp. 194-201.

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U. Shardanand and P. Maes, "Social Information Filtering: Algorithms for Automating 'Word of Mouth'," Proc. ACM CHI95 Conf. Human Factors in Computing Systems, ACM Press, New York, 1995, pp. 210-217.

Digital Library

[8]

Joseph A. Konstan, Bradley N. Miller, David Maltz, Jonathan L. Herlocker, Lee R. Gordon, John Riedl, GroupLens: applying collaborative filtering to Usenet news, Communications of the ACM, v.40 n.3, p.77-87, March 1997

[9]

C.C. Aggarwal et al., "Horting Hatches an Egg: A New Graph-Theoretic Approach to Collaborative Filtering," Proc. 5th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, 1999, ACM Press, New York, 1999, pp. 201-212.

[10]

J.S. Breese, D. Heckerman, and C. Kadie, "Empirical Analysis of Predictive Algorithms for Collaborative Filtering," Proc. 14th Conf. Uncertainty in Artificial Intelligence (UAI-98), Morgan Kaufmann, San Francisco, 1998, pp. 43-52.

[11]

Jonathan L. Herlocker, Joseph A. Konstan, John Riedl, Explaining collaborative filtering recommendations, Proceedings of the 2000 ACM conference on Computer supported cooperative work, p.241-250, December 2000, Philadelphia, Pennsylvania, United States

Digital Library

[12]

N. Good et al., "Combining Collaborative Filtering with Personal Agents for Better Recommendations," Proc. 1999 Conf. Am. Assoc. of Artificial Intelligence (AAAI-99), AAAI Press, Menlo Park, Calif., pp. 439-446.

[13]

Badrul M. Sarwar, Joseph A. Konstan, Al Borchers, Jon Herlocker, Brad Miller, John Riedl, Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system, Proceedings of the 1998 ACM conference on Computer supported cooperative work, p.345-354, November 14-18, 1998, Seattle, Washington, United States

Digital Library

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J. L. Herlocker et al., "An Algorithmic Framework for Performing Collaborative Filtering," Proc. 22nd Ann. Int'l. ACM SIGIR Conf., ACM Press, New York, 2001, pp. 230-237.

[15]

J. Ben Schafer, Joseph Konstan, John Riedi, Recommender systems in e-commerce, Proceedings of the 1st ACM conference on Electronic commerce, p.158-166, November 03-05, 1999, Denver, Colorado, United States

Digital Library

Cited By

Starke AMusto CRapp ASemeraro GTrattner C(2024)“Tell Me Why”: using natural language justifications in a recipe recommender system to support healthier food choicesUser Modeling and User-Adapted Interaction10.1007/s11257-023-09377-834:2(407-440)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s11257-023-09377-8
Musto CStarke ATrattner CRapp ASemeraro G(2021)Exploring the Effects of Natural Language Justifications in Food Recommender SystemsProceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450613.3456827(147-157)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450613.3456827
Dehghani Champiri ZAsemi ASiti Salwah Binti S(2019)Meta-analysis of evaluation methods and metrics used in context-aware scholarly recommender systemsKnowledge and Information Systems10.1007/s10115-018-1324-561:2(1147-1178)Online publication date: 1-Nov-2019
https://dl.acm.org/doi/10.1007/s10115-018-1324-5
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Content-Independent Task-Focused Recommendation
1. Human-centered computing
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

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Published In

cover image IEEE Internet Computing

IEEE Internet Computing Volume 5, Issue 6

November 2001

97 pages

ISSN:1089-7801

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Copyright © Copyright © 2001 IEEE. All Rights Reserved.

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IEEE Educational Activities Department

United States

Publication History

Published: 01 November 2001

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Cited By

Starke AMusto CRapp ASemeraro GTrattner C(2024)“Tell Me Why”: using natural language justifications in a recipe recommender system to support healthier food choicesUser Modeling and User-Adapted Interaction10.1007/s11257-023-09377-834:2(407-440)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s11257-023-09377-8
Musto CStarke ATrattner CRapp ASemeraro G(2021)Exploring the Effects of Natural Language Justifications in Food Recommender SystemsProceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450613.3456827(147-157)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450613.3456827
Dehghani Champiri ZAsemi ASiti Salwah Binti S(2019)Meta-analysis of evaluation methods and metrics used in context-aware scholarly recommender systemsKnowledge and Information Systems10.1007/s10115-018-1324-561:2(1147-1178)Online publication date: 1-Nov-2019
https://dl.acm.org/doi/10.1007/s10115-018-1324-5
Lahlou FBenbrahim HKassou I(2018)Review Aware Recommender SystemInternational Journal of Distributed Artificial Intelligence10.4018/IJDAI.201807010210:2(28-50)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.4018/IJDAI.2018070102
Lahlou FBenbrahim HKassou I(2018)Textual Context Aware Factorization MachinesProceedings of the 2nd International Conference on Smart Digital Environment10.1145/3289100.3289111(64-69)Online publication date: 18-Oct-2018
https://dl.acm.org/doi/10.1145/3289100.3289111
Cheng HRokicki MHerder EBielikova MHerder ECena FDesmarais M(2017)The Influence of City Size on Dietary Choices and Food RecommendationProceedings of the 25th Conference on User Modeling, Adaptation and Personalization10.1145/3079628.3079641(359-360)Online publication date: 9-Jul-2017
https://dl.acm.org/doi/10.1145/3079628.3079641
Modarresi K(2016)Recommendation System Based on Complete PersonalizationProcedia Computer Science10.1016/j.procs.2016.05.37980:C(2190-2204)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.procs.2016.05.379
Modarresi K(2015)Computation of Recommender System Using Localized RegularizationProcedia Computer Science10.1016/j.procs.2015.05.42251:C(2407-2416)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.procs.2015.05.422
Champiri ZShahamiri SSalim S(2015)A systematic review of scholar context-aware recommender systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.09.01742:3(1743-1758)Online publication date: 15-Feb-2015
https://dl.acm.org/doi/10.1016/j.eswa.2014.09.017
Yao WHe JHuang GCao JZhang Y(2015)A Graph-based model for context-aware recommendation using implicit feedback dataWorld Wide Web10.1007/s11280-014-0307-z18:5(1351-1371)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1007/s11280-014-0307-z
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