Abstract
Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user [1]. They exploit adaptive and intelligent systems technologies and have already proved to be valuable for coping with the information overload problem in several application domains. However, while most of the previous research has focused on recommendation techniques and algorithms, i.e., how to compute precise and accurate recommendations, only few studies have stood from users’ angles to consider the processes and issues related to the actual acceptance of the recommendations. Hence, characterizing and evaluating the quality of users’ experience and their subjective attitudes toward the recommendations and the recommendation technologies is an important issue that merits the attention of researchers and practitioners. These issues are important and should be studied both by web technology experts and in the human factor field. The main goal of the first workshop on Decision Making and Recommendation Acceptance issues in Recommender Systems (DEMRA) held at UMAP 2011 was to stimulate the discussion around problems, challenges and research directions about the acceptance of recommendation technologies [2].
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Ricci, F., Semeraro, G., de Gemmis, M., Lops, P. (2012). Decision Making and Recommendation Acceptance Issues in Recommender Systems. In: Ardissono, L., Kuflik, T. (eds) Advances in User Modeling. UMAP 2011. Lecture Notes in Computer Science, vol 7138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28509-7_9
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DOI: https://doi.org/10.1007/978-3-642-28509-7_9
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