Recommender systems: from algorithms to user experience
Since their introduction in the early 1990’s, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advan...
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| Published in: | User modeling and user-adapted interaction Vol. 22; no. 1-2; pp. 101 - 123 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Dordrecht
Springer Netherlands
01.04.2012
Springer Nature B.V |
| Subjects: | |
| ISSN: | 0924-1868, 1573-1391 |
| Online Access: | Get full text |
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| Abstract | Since their introduction in the early 1990’s, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user experience with the recommender. We show through examples that the embedding of the algorithm in the user experience dramatically affects the value to the user of the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective. Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications. |
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| AbstractList | Since their introduction in the early 1990's, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user experience with the recommender. We show through examples that the embedding of the algorithm in the user experience dramatically affects the value to the user of the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective. Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications. Since their introduction in the early 1990's, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user experience with the recommender. We show through examples that the embedding of the algorithm in the user experience dramatically affects the value to the user of the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective. Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications. [PUBLICATION ABSTRACT] |
| Author | Konstan, Joseph A. Riedl, John |
| Author_xml | – sequence: 1 givenname: Joseph A. surname: Konstan fullname: Konstan, Joseph A. email: konstan@cs.umn.edu organization: GroupLens Research, Department of Computer Science and Engineering, University of Minnesota – sequence: 2 givenname: John surname: Riedl fullname: Riedl, John organization: GroupLens Research, Department of Computer Science and Engineering, University of Minnesota |
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| ContentType | Journal Article |
| Copyright | Springer Science+Business Media B.V. 2012 |
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| Keywords | Evaluation Metrics User experience Collaborative filtering Recommender systems |
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| PublicationSubtitle | The Journal of Personalization Research |
| PublicationTitle | User modeling and user-adapted interaction |
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| Snippet | Since their introduction in the early 1990’s, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing... Since their introduction in the early 1990's, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing... |
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| SubjectTerms | Algorithms Collaboration Computer Science Constraining Data mining Electronic commerce End users Evolution Filtering Filtration Focusing Information systems Management of Computing and Information Systems Multimedia Information Systems Neighborhoods Original Paper Ratings & rankings Recommender systems Retarding Similarity measures State of the art Studies User experience User Interfaces and Human Computer Interaction |
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| Title | Recommender systems: from algorithms to user experience |
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