Affiliations: Department of Computer Science, Atma Ram Sanatan Dharma College, University of Delhi, New Delhi, India
Correspondence:
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Corresponding author. Veer Sain Dixit, Department of Computer Science, Atma Ram Sanatan Dharma College,
University of Delhi, New Delhi-110021, India. Tel.: +91 99 1115 5236; E-mail: [email protected].
Abstract: This article presents a scalable and optimized recommender system for e-commerce web sites to maintain a better customer relationship management and survive among its competitors. The proposed system analyses the clickstream data obtained from an ecommerce site and predicts the preference level of the customer for the products clicked but not purchased using efficient classifiers such as decision trees, artificial neural networks and extended trees. Collaborative filtering technique is used to recommend products in which similarity measures are used along with efficient rough set leader clustering algorithm which helps in making accurate and fast recommendations. To determine the effectiveness of the proposed approach, an experimental evaluation has been done which clearly depicts the better performance of the system as compared to conventional approaches.
