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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Acknowledgements We are grateful for the rich and intellectually-stimulating interactions we have had with our many colleagues in the recommender systems research community. We particularly thank our close collaborators Paul Resnick and Loren Terveen, and the students and visitors who have formed the GroupLens Research group. This article is based upon work supported by the National Science Foundation under Grants DGE 95-54517, IIS 96-13960, IIS 97-34442, IIS 99-78717, IIS 01-02229, IIS 03-24851, IIS 05-34420, IIS 05-34939, IIS 08-08692, and IIS 09-64695. We are grateful for their support.
- Collaborative filtering
- Recommender systems
- User experience