Utilizing Association Rules for Improving the Performance of Collaborative Filtering

Utilizing Association Rules for Improving the Performance of Collaborative Filtering

Zainab Khanzadeh (Islamic Azad University - Arak Branch, Iran) and Mehregan Mahdavi (University of Guilan, Iran)
Copyright: © 2012 |Pages: 15
DOI: 10.4018/jeei.2012040102
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

Internet technology has rapidly grown during the last decades. Presently, users are faced with a great amount of information and they need help to find appropriate items in the shortest possible time. Recommender systems were introduced to overcome this problem of overloaded information. They recommend items of interest to users based on their expressed preferences. Major e-commerce companies try to use this technology to increase their sales. Collaborative Filtering is the most promising technique in recommender systems. It provides personalized recommendations according to user preferences. But one of the problems of Collaborative Filtering is cold-start. The authors provide a novel approach for solving this problem through using the attributes of items in order to recommend items to more people for improving e-business activities. The experimental results show that the proposed method performs better than existing methods in terms of the number of generated recommendations and their quality.
Article Preview

2. Recommender Systems

Nowadays, mass of information is increasing faster than our power of processing information. This information is generated as result of producing new books, articles, issues, and so on every year. The largest e-commerce companies offer millions of items for sale. This is a challenge for users, because it is difficult for them to select their required items among the great mass of items. As a result, there is a need for automated recommendation methods. Recommender systems have emerged for this purpose. They include methods for finding important information and knowledge from large datasets with minimal user involvement (Shaw et al., 2009; Schiaffino & Amandi, 2009; Shani et al., 2005).

Recommender systems have recently gained more attention because of their importance for e-commerce applications (Lin, 2000). They tend to understand users’ interests, using their history of buying behavior and suggest items that might interest them (Shaw et al., 2009).

There are different definitions for recommender systems. A recommender system is defined as an information system that is able to analyze past behaviors and present recommendations for current problems1. In the other hand, recommender systems use methods predicting thinking of users, to recognize the most appropriate and nearest item to his/her favorite and then suggest that item to him. In fact, these systems simulate the process that we utilize in our routine life and execute it automatically. Since, in our routine life, we try to find someone nearest to our favorite and ask them about our choices. Many of e-commerce companies use this method and use such services for retaining existing customers and also finding new customers. Hitherto, very different presentation and selling services are being introduced to customers. Presentation personalization systems and item recommendation systems are examples that many e-commerce companies use in order to retain existing customers and find new ones (Akbari, 2008).

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 7: 2 Issues (2017): 1 Released, 1 Forthcoming
Volume 6: 2 Issues (2016)
Volume 5: 2 Issues (2015)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing