Lines in Secure and Robust Recommender Systems

Monitoring security and trust in on-line personalised recommendation systems is now recognised as a key challenge. Noisy data, or maliciously biased data, can significantly skew the system’s output. This paper outlines our research goals, which aim to tackle this issue along a number of lines. Game theoretic techniques are applied to determining bounds on the effect of robustness attacks on recommender systems. Graph theoretic techniques are used to analyse the dataset structure and identify influential users in the application user-group, for filtering purposes. A user profile database is a key component of most on– line e–commerce systems. The database stores user information such as the history of access patterns to the site, product preferences and profile information such as age, profession and so on. This information is used to drive personalisation software to provide individually customised interfaces for each customer that, for example, direct customers to products they are likely to have an interest in. In many cases these databases are open in the sense that they are continuously updated by information gathered from customers. From the site manager’s point–of–view this is good, since it allows for system responses to be dynamically improved as more information about the customer is obtained. However it is also a security concern, since it is often impossible or intractable to confirm the accuracy of customer–inputted information and an accumulation of inaccurate information can seriously degrade the service. From the customer’s perspective, issues of privacy are of significant concern and generally customers are uncomfortable with the collection of detailed personal information. Peer–to–peer systems, such as the eBay auctioning service, involve blind interactions between individuals over the Internet, including agreements to trade. For such systems, the issue of trust between the parties engaged in the transaction is of major concern and much effort has been made to construct reputation reporting algorithms that can automatically calculate the trustworthiness of individuals. Such algorithms typically depend on the user community self–monitoring through a process of individuals rating their satisfaction with the other parties that they Copyright c © 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. have traded with. Hence, such algorithms depend on an open database of information gathered dynamically through user interaction. Originally, personalisation algorithms were evaluated in terms of the quality of service they provided, assuming that databases were accurate. Database quality was measured only in terms of the sparseness of the available dataset, with the assumption that system performance should improve as more data becomes available. However, we have carried out work (O’Mahony, Hurley, & Silvestre 2004b; 2004a; 2005) that has demonstrated that not only is the data quality an important concern to personalisation algorithms, relatively small quantities of maliciously tailored data, entered into systems through open user interfaces, can drastically degrade the performance of the system. This work, along with the work of others (Lam, Frankowski, & Riedl 2005; Burke, Mobasher, & Bhaumik 2005), is part of a general upsurge in interest in the community into issues of trust and security in personalisation software. In the last number of years, we have carried out an intensive investigation of robustness in the context of the collaborative filtering recommendation algorithm. We have shown the degree to which robustness is a problem and have proposed some ways to counter the robustness problem. All solutions rely on filtering unusual data from the database. However, this is in opposition to the general philosophy of personalisation. Customers’ individuality is determined by how they differ from the norm. There is therefore a danger that, particularly as databases scale to large numbers of customers, filtering algorithms employed to increase the security and robustness of the system may in fact reduce the system’s ability to provide a truly personalised service. Hence, the general question that this research will attempt to answer is how to tackle robustness and security while maintaining personalised performance. In summary, our research addresses the problem of developing personalisation algorithms that are: • robust, in the sense that they can operate effectively with noisy data; • secure, in the sense that they cannot be manipulated by potentially malicious end–users; • user–centric, in that they take into account privacy re-