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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Vydáno v:User modeling and user-adapted interaction Ročník 22; číslo 1-2; s. 101 - 123
Hlavní autoři: Konstan, Joseph A., Riedl, John
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.04.2012
Springer Nature B.V
Témata:
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
United States > US
Evaluation
Metrics
User experience
Collaborative filtering
Recommender systems
ISSN:0924-1868, 1573-1391
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Shrnutí: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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ISSN:0924-1868
1573-1391
DOI:10.1007/s11257-011-9112-x