Making recommendations better: An analytic model for Human-Recommender Interaction

Sean M. McNee; John Riedl; Joseph A. Konstan

doi:10.1145/1125451.1125660

Making recommendations better: An analytic model for Human-Recommender Interaction

Sean M. McNee, John Riedl, Joseph A. Konstan

Computer Science and Engineering

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

101 Scopus citations

Abstract

Recommender systems do not always generate good recommendations for users. In order to improve recommender quality, we argue that recommenders need a deeper understanding of users and their information seeking tasks. Human-Recommender Interaction (HRI) provides a framework and a methodology for understanding users, their tasks, and recommender algorithms using a common language. Further, by using an analytic process model, HRI becomes not only descriptive, but also constructive. It can help with the design and structure of a recommender system, and it can act as a bridge between user information seeking tasks and recommender algorithms.

Original language	English (US)
Title of host publication	CHI'06 Extended Abstracts on Human Factors in Computing Systems, CHI EA'06
Pages	1103-1108
Number of pages	6
DOIs	https://doi.org/10.1145/1125451.1125660
State	Published - 2006
Event	Conference on Human Factors in Computing Systems, CHI EA 2006 - Montreal, QC, Canada Duration: Apr 22 2006 → Apr 27 2006

Publication series

Name	Conference on Human Factors in Computing Systems - Proceedings

Other

Other	Conference on Human Factors in Computing Systems, CHI EA 2006
Country/Territory	Canada
City	Montreal, QC
Period	4/22/06 → 4/27/06

Keywords

Collaborative filtering
Human-Recommender Interaction
Information seeking
Personalization
Recommender systems
User-centered design

Publisher link

10.1145/1125451.1125660

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Making recommendations better: An analytic model for Human-Recommender Interaction. / McNee, Sean M.; Riedl, John; Konstan, Joseph A.
CHI'06 Extended Abstracts on Human Factors in Computing Systems, CHI EA'06. 2006. p. 1103-1108 (Conference on Human Factors in Computing Systems - Proceedings).

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

McNee, SM, Riedl, J & Konstan, JA 2006, Making recommendations better: An analytic model for Human-Recommender Interaction. in CHI'06 Extended Abstracts on Human Factors in Computing Systems, CHI EA'06. Conference on Human Factors in Computing Systems - Proceedings, pp. 1103-1108, Conference on Human Factors in Computing Systems, CHI EA 2006, Montreal, QC, Canada, 4/22/06. https://doi.org/10.1145/1125451.1125660

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title = "Making recommendations better: An analytic model for Human-Recommender Interaction",

abstract = "Recommender systems do not always generate good recommendations for users. In order to improve recommender quality, we argue that recommenders need a deeper understanding of users and their information seeking tasks. Human-Recommender Interaction (HRI) provides a framework and a methodology for understanding users, their tasks, and recommender algorithms using a common language. Further, by using an analytic process model, HRI becomes not only descriptive, but also constructive. It can help with the design and structure of a recommender system, and it can act as a bridge between user information seeking tasks and recommender algorithms.",

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series = "Conference on Human Factors in Computing Systems - Proceedings",

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note = "Conference on Human Factors in Computing Systems, CHI EA 2006 ; Conference date: 22-04-2006 Through 27-04-2006",

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AB - Recommender systems do not always generate good recommendations for users. In order to improve recommender quality, we argue that recommenders need a deeper understanding of users and their information seeking tasks. Human-Recommender Interaction (HRI) provides a framework and a methodology for understanding users, their tasks, and recommender algorithms using a common language. Further, by using an analytic process model, HRI becomes not only descriptive, but also constructive. It can help with the design and structure of a recommender system, and it can act as a bridge between user information seeking tasks and recommender algorithms.

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KW - Information seeking

KW - Personalization

KW - Recommender systems

KW - User-centered design

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Making recommendations better: An analytic model for Human-Recommender Interaction

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Keywords

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