A collaborative system for personalized recommendation in an online supermarket

Identifier:  TFM:1633

Handle: https://hdl.handle.net/20.500.11797/TFM1633

Authors:  Gursoy, Semiha

Abstract:
The Web has become the significant route of trade. As the number of options available online increased, the necessity of computer systems to assist users in purchasing decisions has arisen. These systems are known as personalized recommender systems. This work focuses on the construction of a personalized recommender system for a Catalan food supply company. Using a collaborative filtering approach, this system integrates the users’ purchase history and relevant contextual data to generate recommendations. The experimental results validate the efficacy of the recommender system in identifying potential items of interest for users.