
Conference Paper: Improved Collaborative Filtering.
[Show abstract] [Hide abstract]
ABSTRACT: We consider the interactive model of collaborative filtering, where each member of a given set of users has a grade for each object in a given set of objects. The users do not know the grades at start, but a user can probe any object, thereby learning her grade for that object directly. We describe reconstruction algorithms which generate good estimates of all user grades (“preference vectors”) using only few probes. To this end, the outcomes of probes are posted on some public “billboard”, allowing users to adopt results of probes executed by others. We give two new algorithms for this task under very general assumptions on user preferences: both improve the best known query complexity for reconstruction, and one improving resilience in the presence of many users with esoteric taste.Algorithms and Computation  22nd International Symposium, ISAAC 2011, Yokohama, Japan, December 58, 2011. Proceedings; 01/2011 
Conference Paper: Recommender systems with nonbinary grades.
[Show abstract] [Hide abstract]
ABSTRACT: We consider the interactive model of recommender systems, in which users are asked about just a few of their preferences, and in return the system outputs an approximation of all their preferences. The measure of performance is the probe complexity of the algorithm, defined to be the maximal number of answers any user should provide (probe complexity typically depends inversely on the number of users with similar preferences and on the quality of the desired approximation). Previous interactive recommendation algorithms assume that user preferences are binary, meaning that each object is either "liked" or "disliked" by each user. In this paper we consider the general case in which users may have a more refined scale of preference, namely more than two possible grades. We show how to reduce the nonbinary case to the binary one, proving the following results. For discrete grades with s possible values, we give a simple deterministic reduction that preserves the approximation properties of the binary algorithm at the cost of increasing probe complexity by factor s. Our main result is for the general case, where we assume that user grades are arbitrary real numbers. For this case we present an algorithm that preserves the approximation properties of the binary algorithm while incurring only polylogarithmic overhead.SPAA 2011: Proceedings of the 23rd Annual ACM Symposium on Parallelism in Algorithms and Architectures, San Jose, CA, USA, June 46, 2011 (Colocated with FCRC 2011); 01/2011 
Conference Paper: DSybil: Optimal SybilResistance for Recommendation Systems
[Show abstract] [Hide abstract]
ABSTRACT: Recommendation systems can be attacked in various ways, and the ultimate attack form is reached with a sybil attack, where the attacker creates a potentially unlimited number of sybil identities to vote. Defending against sybil attacks is often quite challenging, and the nature of recommendation systems makes it even harder. This paper presents DSybil, a novel defense for diminishing the influence of sybil identities in recommendation systems. DSybil provides strong provable guarantees that hold even under the worstcase attack and are optimal. DSybil can defend against an unlimited number of sybil identities over time. DSybil achieves its strong guarantees by i) exploiting the heavytail distribution of the typical voting behavior of the honest identities, and ii) carefully identifying whether the system is already getting "enough help" from the (weighted) voters already taken into account or whether more "help" is needed. Our evaluation shows that DSybil would continue to provide highquality recommendations even when a million node botnet uses an optimal strategy to launch a sybil attack.Security and Privacy, 2009 30th IEEE Symposium on; 06/2009
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.