A Novel Method to Calculate Click Through Rate for Sponsored Search

TL;DR

This paper introduces a new CTR calculation method for sponsored search that uses relative ranking to reduce the impact of fraudulent clicks, improving accuracy over traditional methods.

Contribution

The proposed algorithm links clicks to a total click count, making CTR more robust against fraudulent click manipulation compared to existing fixed-window approaches.

Findings

The new method reduces false positives from fraudulent clicks.

It maintains stable CTR estimates during click anomalies.

The approach enhances the reliability of ad auction mechanisms.

Abstract

Sponsored search adopts generalized second price (GSP) auction mechanism which works on the concept of pay per click which is most commonly used for the allocation of slots in the searched page. Two main aspects associated with GSP are the bidding amount and the click through rate (CTR). The CTR learning algorithms currently being used works on the basic principle of (#clicks_i/ #impressions_i) under a fixed window of clicks or impressions or time. CTR are prone to fraudulent clicks, resulting in sudden increase of CTR. The current algorithms are unable to find the solutions to stop this, although with the use of machine learning algorithms it can be detected that fraudulent clicks are being generated. In our paper, we have used the concept of relative ranking which works on the basic principle of (#clicks_i /#clicks_t). In this algorithm, both the numerator and the denominator are linked. As #clicks_t is higher than previous algorithms and is linked to the #clicks_i, the small change in the clicks which occurs in the normal scenario have a very small change in the result but in case of fraudulent clicks the number of clicks increases or decreases rapidly which will add up with the normal clicks to increase the denominator, thereby decreasing the CTR.