Rajat Bhattacharjee

Avoiding ballot stuffing in eBay-like reputation systems

We present a preliminary study on the robustness of binary feedback reputation systems (e.g. eBay) to ballot stuffing and bad mouthing. In a feedback based reputation system, a seller can collude with other buyers to undertake fake transactions in order to enhance her reputation. This problem is referred to as ballot stuffing. A seller can also be targeted by a group of buyers to deliberately lower her reputation. This problem is referred to as bad mouthing. For the reputations to be meaningful, any practical reputation system needs to be resistant to these problems. We use a simplified model to give an explicit relation between the reputation premium and the transaction cost that needs to hold in order to avoid ballot stuffing. Thus we draw attention to the necessity of transaction costs for a well functioning reputation system. Our conclusions are confirmed by empirical experiments on eBay.

Instability of FIFO at Arbitrarily Low Rates in the Adversarial Queueing Model

We study the stability of the commonly used packet forwarding protocol, FIFO (first in first out), in the adversarial queueing model. We prove that FIFO can become unstable, i.e., lead to unbounded buffer-occupancies and queueing delays, at arbitrarily low injection rates. In order to demonstrate instability at rate

An incentive-based architecture for social recommendations

r

Algorithms and incentives for robust ranking

, we use a network of size

Providing relevance- and diversity-influenced advertisements including filtering

\tilde{O}(1/r)

Query suggestions with high utility

.

Query suggestions with high diversity

We present an incentive-based architecture for providing recommendations in a social network. We maintain a distinct reputation system for each individual and we rely on users to identify appropriate correlations and rate the items using a system-provided recommendation language. The key idea is to design an incentive structure and a ranking system such that any inaccuracy in the recommendations implies the existence of a profitable arbitrage opportunity, hence making the system resistant to malicious spam and presentation bias. We also show that, under mild assumptions, our architecture provides users with incentive to minimize the Kullback-Leibler divergence between the ratings and the actual item qualities, quickly driving the system to an equilibrium state with accurate recommendations.