Group-based recipe recommendations: analysis of data aggregation strategies

Shlomo Berkovsky; Jill Freyne

doi:10.1145/1864708.1864732

Group-based recipe recommendations: analysis of data aggregation strategies

Shlomo Berkovsky, Jill Freyne

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

235 Citations (Scopus)

Abstract

Collaborative filtering recommendations were designed primarily for individual user models and recommendations. However, nowadays more and more scenarios evolve, in which the recommended items are consumed by groups of users rather than by individuals. This raises the need to uncover the most appropriate group-based collaborative filtering recommendation strategy. In this work we investigate the use of aggregated group data in collaborative filtering recipe recommendations. We present results of a study that exploits recipe ratings provided by families of users, in order to evaluate the accuracy of several group recommendation strategies and weighting models, and analyze the impact of switching strategies, data aggregation heuristics, and group characteristics on the performance of recommendations.

Original language	English
Title of host publication	Proceedings of the fourth ACM conference on Recommender systems, RecSys '10
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Pages	111-118
Number of pages	8
ISBN (Electronic)	9781605586090
DOIs	https://doi.org/10.1145/1864708.1864732
Publication status	Published - 2010
Externally published	Yes
Event	4th ACM Conference on Recommender Systems, RecSys 2010 - Barcelona, Spain Duration: 26 Sept 2010 → 30 Sept 2010

Conference

Conference	4th ACM Conference on Recommender Systems, RecSys 2010
Country/Territory	Spain
City	Barcelona
Period	26/09/10 → 30/09/10

Keywords

recipe recommendations
group recommendations
collaborative filtering

Access to Document

10.1145/1864708.1864732

Cite this

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title = "Group-based recipe recommendations: analysis of data aggregation strategies",

abstract = "Collaborative filtering recommendations were designed primarily for individual user models and recommendations. However, nowadays more and more scenarios evolve, in which the recommended items are consumed by groups of users rather than by individuals. This raises the need to uncover the most appropriate group-based collaborative filtering recommendation strategy. In this work we investigate the use of aggregated group data in collaborative filtering recipe recommendations. We present results of a study that exploits recipe ratings provided by families of users, in order to evaluate the accuracy of several group recommendation strategies and weighting models, and analyze the impact of switching strategies, data aggregation heuristics, and group characteristics on the performance of recommendations.",

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author = "Shlomo Berkovsky and Jill Freyne",

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pages = "111--118",

booktitle = "Proceedings of the fourth ACM conference on Recommender systems, RecSys '10",

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note = "4th ACM Conference on Recommender Systems, RecSys 2010 ; Conference date: 26-09-2010 Through 30-09-2010",

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Berkovsky, S & Freyne, J 2010, Group-based recipe recommendations: analysis of data aggregation strategies. in Proceedings of the fourth ACM conference on Recommender systems, RecSys '10. Association for Computing Machinery (ACM), New York, pp. 111-118, 4th ACM Conference on Recommender Systems, RecSys 2010, Barcelona, Spain, 26/09/10. https://doi.org/10.1145/1864708.1864732

Group-based recipe recommendations: analysis of data aggregation strategies. / Berkovsky, Shlomo; Freyne, Jill.
Proceedings of the fourth ACM conference on Recommender systems, RecSys '10. New York: Association for Computing Machinery (ACM), 2010. p. 111-118.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Group-based recipe recommendations

T2 - 4th ACM Conference on Recommender Systems, RecSys 2010

AU - Berkovsky, Shlomo

AU - Freyne, Jill

PY - 2010

Y1 - 2010

N2 - Collaborative filtering recommendations were designed primarily for individual user models and recommendations. However, nowadays more and more scenarios evolve, in which the recommended items are consumed by groups of users rather than by individuals. This raises the need to uncover the most appropriate group-based collaborative filtering recommendation strategy. In this work we investigate the use of aggregated group data in collaborative filtering recipe recommendations. We present results of a study that exploits recipe ratings provided by families of users, in order to evaluate the accuracy of several group recommendation strategies and weighting models, and analyze the impact of switching strategies, data aggregation heuristics, and group characteristics on the performance of recommendations.

AB - Collaborative filtering recommendations were designed primarily for individual user models and recommendations. However, nowadays more and more scenarios evolve, in which the recommended items are consumed by groups of users rather than by individuals. This raises the need to uncover the most appropriate group-based collaborative filtering recommendation strategy. In this work we investigate the use of aggregated group data in collaborative filtering recipe recommendations. We present results of a study that exploits recipe ratings provided by families of users, in order to evaluate the accuracy of several group recommendation strategies and weighting models, and analyze the impact of switching strategies, data aggregation heuristics, and group characteristics on the performance of recommendations.

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KW - group recommendations

KW - collaborative filtering

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DO - 10.1145/1864708.1864732

M3 - Conference proceeding contribution

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BT - Proceedings of the fourth ACM conference on Recommender systems, RecSys '10

PB - Association for Computing Machinery (ACM)

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ER -

Group-based recipe recommendations: analysis of data aggregation strategies

Abstract

Conference

Keywords

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