Recommender systems have been served to assist decision making by recommending a list of items the end users. Multi-criteria recommender system (MCRS) is type which enhance recommendation performance taking user preferences on multiple criteria. Traditional algorithms for MCRS usually predict ratings these criteria, and finally estimate overall rating different aggregation functions. In this pa...

Given a collection of N items with some unknown underlying ranking, we examine how to use pairwise comparisons to determine the top ranked items in the set. Resolving the top items from pairwise comparisons has application in diverse fields ranging from recommender systems to image-based search to protein structure analysis. In this paper we introduce techniques to resolve the top ranked items ...

Privacy issues are an undying obstacle to the adoption of recommender systems, because recommender systems critically rely on their users to disclose information about themselves. While there exist several technical solutions to reduce the exposure of such personal information (e.g. client-side personalization, homomorphic encryption, k-anonymity), the concept of privacy is an inherently human ...

Privacy issues are an undying obstacle to the adoption of recommender systems, because recommender systems critically rely on their users to disclose information about themselves. While there exist several technical solutions to reduce the exposure of such personal information (e.g. client-side personalization, homomorphic encryption, k-anonymity), the concept of privacy is an inherently human ...

The importance of accurate recommender systems has been widely recognized by academia and industry. However, the recommendation quality is still rather low. Recently, a linear sparse and low-rank representation of the user-item matrix has been applied to produce Top-N recommendations. This approach uses the nuclear norm as a convex relaxation for the rank function and has achieved better recomm...