© 2017, IJARCSMS All Rights Reserved 7 | P a g e ISSN: 2321-7782 (Online) e-ISJN: A4372-3114 Impact Factor: 6.047 Volume 5, Issue 5, May 2017 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online at: www.ijarcsms.com Efficient Rank Calculation of True Reputation for a Product with a limited number of datasets N. S. Adambi 1 M.Tech Scholar Department Of CS St.Marys Group of Institutions Guntur Chebrole Guntur(Dt), Andhra Pradesh, 522212 – India E. Raveendra Reddy 2 Asst Professor Department Of CS St.Marys Group of Institutions Guntur Chebrole Guntur(Dt), Andhra Pradesh, 522212 – India Abstract: The average of customer ratings on a product, which we call a reputation, is one of the key factors in online purchasing decisions. There is, however, no guarantee of the trustworthiness of a reputation since it can be manipulated rather easily. In this paper, we define false reputation as the problem of a reputation being manipulated by unfair ratings and design a general framework that provides trustworthy reputations. For this purpose, we propose Trust-reputation, an algorithm that iteratively adjusts a reputation based on the confidence of customer ratings. We also show the effectiveness of Trust-reputation through extensive experiments in comparisons to state-of-the-art approaches. I. INTRODUCTION While using online shopping channels, consumers share their purchasing experiences regarding both goods and services with other potential buyers via evaluation. The most common way for consumers to express their level of satisfaction with their purchases is through online ratings. The overall buyers’ satisfaction is quantified as the aggregated score of all ratings and is available to all potential buyers. In this paper, we call this aggregated score for a product its reputation. The reputation of a product plays an important role as a guide for potential buyers and significantly influences consumers’ final purchasing decisions. “Is the Product’s Reputation Trustworthy?” Reputation is the score of a product obtained through collective intelligence, i.e., the result of collaboration between many individuals. The proposed framework, on the other hand, uses all ratings .It evaluates the level of trustworthiness (confidence) of each rating and adjusts the reputation based on the confidence of ratings. We have developed an algorithm that iteratively adjusts a reputation based on the confidence of customer ratings. By adjusting a reputation based on the confidence scores of all ratings, the proposed algorithm calculates the reputation without the risk of omitting ratings by normal users while reducing the influence of unfair ratings by abusers. We call this algorithm, which solves the false reputation problem by computing the true reputation, TRUE-REPUTATION. The computation of a trustworthy reputation starts by measuring the confidence of a rating. We have surveyed previous social science studies that analyzed the characteristics of reliable online information and adopted three key characteristics that are suitable for determining the confidence of a rating [6], [23]. To determine the confidence of a rating, therefore, we have adopted three key factors of activity, objectivity, and consistency and defined these factors in the context of online ratings. First, the user who rates more items displays a higher level of activity. The above description of activity implies that the activity is defined by the amount of interactions between an information producer and the users obtaining his information. There exist, however, no interactions between users in an online rating system; instead, there are actions by users on products. Therefore, we measure user activity in an online rating system based on the amount of actions by the user on products (i.e., the number of products he rates). The objectivity of a rating is calculated based on the deviation of the “rating” from the “reputation” of the product. The difficulty in