Abstract
In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed for providing dynamic recommendations to users that can be used for customizing a website is proposed. The proposed WebBluegillRecom-annealing dynamic recommender uses swarm intelligence from the foraging behavior of a bluegill fish. It overcomes the information overload by handling dynamic behaviors of users. Our dynamic recommender system was compared against traditional collaborative filtering systems. The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems. Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations.
Highlights
The customer relationship management (CRM) entails the interaction of an organization with the current and future customers
WebBluegillRecom-annealing dynamic recommender system is capable of handling dynamic data
The results obtained are compared with the traditional collaborative filtering system
Summary
The customer relationship management (CRM) entails the interaction of an organization with the current and future customers. The information regarding customer’s interest and behavior helps a website administrator to personalize or customize a web page for a user Such increased usage of business websites online creates a huge amount of web usage information to manage causing information overload. The major challenges of online web usage data, in addition to information overloading, are its high dimensionality and dynamic nature caused by thousands of users. The online usage data represents the interest of human beings that are highly dynamic in nature These dynamic behaviors may be due to the changes in the user’s interest or due to the addition or deletion of web pages in a website. The results of performance evaluation show that the proposed dynamic recommender system gives better predictions in less time without losing the quality in terms of coverage, precision, F1 measure, and scalability, compared with the traditional approaches.
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