That consumers share similar tastes on some products does not guarantee their agreement on other products. Therefore, both similarity and dierence should be taken into account for a more rounded view on consumer preferences. This manuscript focuses on mining this diversity of consumer preferences from two perspectives, namely 1) between consumers and 2) between products. Diversity of preferences between consumers is studied in the context of recommendation systems. In some preference models, measuring similarities in preferences between two consumers plays the key role. These approaches assume two consumers would share certain degree of similarity on any products, ignoring the fact that the similarity may vary across products. We take one step further by measuring different degrees of similarity between two consumers.
data mining, database application, recommender systems, collaborative filtering, bundling, profit maximization
PhD in Information Systems
Categorical Data Analysis | Databases and Information Systems
LAUW, Hady Wirawan
Singapore Management University
City or Country
DO, Ha Loc.
Mining diverse consumer preferences for bundling and recommendation. (2017). 1-180. Dissertations and Theses Collection.
Available at: http://ink.library.smu.edu.sg/etd_coll_all/18
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Available for download on Wednesday, July 01, 2020