Since the inception of the World Wide Web, the amount of data present on websites and internet infrastructure has grown exponentially that researchers continuously develop new and more efficient ways of sorting and presenting information to end-users. Particular websites, such as e-commerce websites, filter data with the help of recommender systems. Over the years, methods have been developed to improve recommender accuracy, yet developers face a problem when new items or users enter the system. With little to no information on user or item preferences, recommender systems struggle generating accurate predictions. This is the cold-start problem. Ackoff defines information as data structured around answers to the question words: what, where, when, who and how many. This paper explores how Ackoff’s definition of information might improve accuracy and alleviate cold-start conditions when applied to the neighborhood model of collaborative filtering (Ackoff, 1989, p. 3).
Library of Congress Subject Headings
Recommender systems (Information filtering)
Information Sciences and Technologies (MS)
Department, Program, or Center
Information Sciences and Technologies (GCCIS)
Matus Nicodemos, Marcelo, "Information-Based Neighborhood Modeling" (2017). Thesis. Rochester Institute of Technology. Accessed from
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