Collaborative Recommendation: A Robustness Analysis (2003) [41 citations — 2 self]
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author = {Michael O'Mahony and Neil Hurley and Nicholas Kushmerick and Guénolé Silvestre},
title = {Collaborative Recommendation: A Robustness Analysis},
journal = {ACM Transactions on Internet Technology},
year = {2003},
volume = {4},
pages = {2004}
}
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Abstract:
this article is organised as follows. We begin with a discussion of collaborative recommendation and a formalisation of the notions of robustness and the perturbations with which we are concerned (Section 2). We then analyse robustness from both the accuracy and stability perspectives. Regarding accuracy, in Section 3 we formalise robustness in machine learnings terms, and introduce a novel form of class noise that models an interesting suite of attacks. We develop two models that predict the change in accuracy as a function of the number of fake ratings that have been inserted into the customer/product matrix. Regarding stability, we present a framework that describes the stability of a recommendation system subjected to various forms of attack (Section 4). In both cases, we empirically evaluate our predications against several real-world data-sets. We conclude with a description of related work (Section 3.4) and with a summary of our results and a discussion of several open issues (Section 5)
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