ORFEL: efficient detection of defamation or illegitimate promotion in online recommendation

TL;DR

ORFEL is a scalable and efficient algorithm designed to detect fraudulent low-valued recommendations and illegitimate promotions in online platforms by analyzing user-product interaction graphs, addressing both defamation and promotion issues.

Contribution

It introduces a novel, scalable algorithmic approach using vertex-centric asynchronous processing to detect recommendation fraud in large bipartite graphs, considering both defamation and illegitimate promotion.

Findings

Detected over 95% of potential attacks.

At least two orders of magnitude faster than previous methods.

Effectively addresses both defamation and illegitimate promotion.

Abstract

What if a successful company starts to receive a torrent of low-valued (one or two stars) recommendations in its mobile apps from multiple users within a short (say one month) period of time? Is it legitimate evidence that the apps have lost in quality, or an intentional plan (via lockstep behavior) to steal market share through defamation? In the case of a systematic attack to one's reputation, it might not be possible to manually discern between legitimate and fraudulent interaction within the huge universe of possibilities of user-product recommendation. Previous works have focused on this issue, but none of them took into account the context, modeling, and scale that we consider in this paper. Here, we propose the novel method Online-Recommendation Fraud ExcLuder (ORFEL) to detect defamation and/or illegitimate promotion of online products by using vertex-centric asynchronous parallel processing of bipartite (users-products) graphs. With an innovative algorithm, our results demonstrate both efficacy and efficiency -- over 95% of potential attacks were detected, and ORFEL was at least two orders of magnitude faster than the state-of-the-art. Over a novel methodology, our main contributions are: (1) a new algorithmic solution; (2) one scalable approach; and (3) a novel context and modeling of the problem, which now addresses both defamation and illegitimate promotion. Our work deals with relevant issues of the Web 2.0, potentially augmenting the credibility of online recommendation to prevent losses to both customers and vendors.