Chiron: A Robust Recommendation System with Graph Regularizer

Recommendation systems have been widely used by commercial service providers for giving suggestions to users. Collaborative filtering (CF) systems, one of the most popular recommendation systems, utilize the history of behaviors of the aggregate user-base to provide individual recommendations and are effective when almost all users faithfully express their opinions. However, they are vulnerable to malicious users biasing their inputs in order to change the overall ratings of a specific group of items. CF systems largely fall into two categories - neighborhood-based and (matrix) factorization-based - and the presence of adversarial input can influence recommendations in both categories, leading to instabilities in estimation and prediction. Although the robustness of different collaborative filtering algorithms has been extensively studied, designing an efficient system that is immune to manipulation remains a significant challenge. In this work we propose a novel "hybrid" recommendation system with an adaptive graph-based user/item similarity-regularization - "Chiron". Chiron ties the performance benefits of dimensionality reduction (through factorization) with the advantage of neighborhood clustering (through regularization). We demonstrate, using extensive comparative experiments, that Chiron is resistant to manipulation by large and lethal attacks

Predictability Issues in Recommender Systems Based on Web Usage Behavior towards Robust Collaborative Filtering

This paper examines the effect of Recommender Systems in security oriented issues. Currently research has begun to evaluate the vulnerabilities and robustness of various collaborative recommender techniques in the face of profile injection and shilling attacks. Standard collaborative filtering algorithms are vulnerable to attacks. The robustness of recommender system and the impact of attacks are well suited this study and examined in this paper. The predictability issues and the various attack strategies are also discussed. Based on KNN the robustness of the recommender system were examined and the sensitivity of the rating given by the users are also analyzed. Furthermore the robust PLSA also considered for the work

A Survey on True-reputation Algorithm for Trustworthy Online Rating System

The average of customer ratings on a product, which we call a reputation, is one of the key factors in online shoping. The common way for customers to express their satisfaction level with their purchases is through online ratings. The overall buyer?s satisfaction is quantified as the aggregated score of all ratings and is available to all buyers. This average score and reputation of a product acts as a guide for online buyers and highly influences consumer?s final purchase decisions. The trustworthiness of a reputation can be achieved when a large number of buyers involved in ratings with honesty. If some users wantedly give unfair ratings to a item, especially when few users have participated, the reputation of the product could easily be modified. In order to improve the trustworthiness of the products in e-commerce sites a new model is proposed with a true - reputation algorithm that repeatedly adjusts the reputation based on the confidence of the user ratings

A robust reputation-based location-privacy recommender system using opportunistic networks

Location-sharing services have grown in use commensurately with the increasing popularity of smart phones. As location data can be sensitive, it is important to preserve people’s privacy while using such services, and so location-privacy recommender systems have been proposed to help people configure their privacy settings.These recommenders collect and store people’s data in a centralised system, but these themselves can introduce new privacy threats and concerns.In this paper, we propose a decentralised location-privacy recommender system based on opportunistic networks. We evaluate our system using real-world location-privacy traces, and introduce a reputation scheme based on encounter frequencies to mitigate the potential effects of shilling attacks by malicious users. Experimental results show that, after receiving adequate data, our decentralised recommender system’s performance is close to the performance of traditional centralised recommender systems (3% difference in accuracy and 1% difference in leaks). Meanwhile, our reputation scheme significantly mitigates the effect of malicious users’input (from 55% to 8% success) and makes it increasingly expensive to conduct such attacks.Postprin