Mining diverse consumer preferences for bundling and recommendation

• Main Author: DO, Ha Loc
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: data mining; database application; recommender systems; collaborative filtering; bundling; profit maximization; Categorical Data Analysis; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/etd_coll_all/18; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1036&context=etd_coll_all
• Institution: Singapore Management University

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.