A METHODOLOGY FOR DESIGNING A RECOMMENDER SYSTEM BASED ON CUSTOMER PREFERENCES

DS 68-9: Proceedings of the 18th International Conference on Engineering Design (ICED 11), Impacting Society through Engineering Design, Vol. 9: Design Methods and Tools pt. 1, Lyngby/Copenhagen, Denmark, 15.-19.08.2011

Year: 2011
Editor: Culley, S.J.; Hicks, B.J.; McAloone, T.C.; Howard, T.J. & Dong, A.
Author: Jomaa, Inčs; Poirson, Emilie; Da Cunha, Catherine; Petiot, Jean-François
Series: ICED
Section: Design Methods and Tools Part 1
Page(s): 305-313

Abstract

This paper presents a contribution to design an online preference based system. The objective of the system is to assist a customer in the products selection process. Current e-commerce recommendation systems assist customers in this process. Nevertheless, quality of the recommendations produced remains a real challenge. There are products that are by mistake recommended to customers and inversely. This paper focuses on these quality and relevance of recommendations. More than product objective characteristics, the customer’s choice is also based on his/her perceptive expectations. Therefore, to be considered as relevant, products recommendations must reach customer’s expectations and particularly perceptive ones, sometimes spontaneously, without specific request. Collaborative filtering and neighbourhood formation are the main tools used. The cluster of “perceptive” neighbours containing the active customer share common perceptive preferences and can guide the propositions. The application case is the comic. The aim is to propose to a customer a “good” product. A test procedure enabling the validation of this algorithm is to be set.

Keywords: SEMANTICS; RECOMMENDER SYSTEMS; PREFERENCE; EMOTIONS; COLLABORATIVE FILTERING

Download

Please sign in to your account

This site uses cookies and other tracking technologies to assist with navigation and your ability to provide feedback, analyse your use of our products and services, assist with our promotional and marketing efforts, and provide content from third parties. Privacy Policy.