A Secure Cold-Start Online Product Recommendation System with Reputation Defense Technique Using Anomaly Detection


Authors : Bhavya Boppana,Shravya Vuppala,R.Subash.

Volume/Issue : Volume 2 - 2017, Issue 4 - April

Google Scholar : https://goo.gl/levB9J

Scribd : https://goo.gl/FechVn

Thomson Reuters ResearcherID : https://goo.gl/3bkzwv

Connecting Social Media to E-Commerce System with Reputation System and making the proposed system more secure with Anomaly based technique. In the existing paper, the author proposed learning both clients’ and products’ feature representations from data collected from e-commerce websites. But they did not considered about the False Ratings or Misbehaving Clients/False client’s value. The Concept of Reputation System was introduced to have a better outcome of a metric have encapsulating reputation for a particular domain for each identity within the system. This reputation systems objective is to produce an accurate assessment in the model of different factors but not to adversarial environments. Thus we make a particular focus on introducing the reputation system model into the existing system and propose a new secure model. The proposed system commenced to provide a secure framework that grants for general decomposition of existing reputation system model. Confident and Secure reputation modeling system is a vital aspect in managing risk and building customer satisfaction in e-commerce system. Miserable, the existing Cold start model does not considered the reputation model and uses only the simple feedbacks and comments issues from Online Social Networking client. Foresaid schemes are known easily to cheat/deceive and which also does not provide needed security or protection against several types of fraud/attacks. So we propose an anomaly detection technique for finding unfair recommendation in online Product Recommendation Model.

Keywords : E-commerce recommendation, Anomaly detection, similarity measures, preference feature construction.

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