A Commerce Recommender System for Improving Customer Relationship Management in Shopping Centres

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2014 by IJETT Journal
Volume-13 Number-4
Year of Publication : 2014
Authors : Theresa Rani Joseph , Smitha Jacob
  10.14445/22315381/IJETT-V13P234

MLA 

Theresa Rani Joseph , Smitha Jacob. "A Commerce Recommender System for Improving Customer Relationship Management in Shopping Centres", International Journal of Engineering Trends and Technology (IJETT), V13(4),158-165 July 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

The potential application of data mining in recommender systems is a widely researched topic. The paper proposes a commerce recommender system which provides shopping recommendations for improving customer relationship within a shopping centre. A dual recommender system has been designed which includes a Personalized Recommender System (PRS) and a Generic Recommender System (GRS). PRS provides personalized recommendations based on each user’s previous shopping patterns. GRS on the other hand makes use of a similarity measuring algorithm for recommending shops containing products similar to those in another shop. The work focuses on the data mining phase of KDD. The frequent pattern mining for each user as well as similarity measurement algorithm is performed on a prototype database for the shopping centre under consideration.

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Keywords
Personalized Recommender System, Generic Recommender System, Frequent Pattern Mining, Similarity Measuring Model, Customer Relationship Management.