A recommender system based on ranks is proposed, where an expert’s ranking of a set of objects and a user’s ranking of a subset of those objects are combined to make a prediction of the user’s ranking of all objects. The rankings are assumed to be induced by latent continuous variables corresponding to the grades assigned by the expert and the user to the objects. The dependence between the exp...

recently, farzipoor saen [journal of the operational research society, 60(11), 1575–1582 (2009)] proposed a method based on data envelopment analysis to identify optimistic efficient suppliers in the presence of nondiscretionary factors-imprecise data. this short communication aims at showing a computational error in computing the value of preference intensity parameter in farzipoor saen’s [1] ...

Using Yelp data, we built a restaurant recommendation system for individuals and groups. For each of 4.9k individual Yelp users, we create a ranking SVM model with features encompassing users’ food preferences and dietary restrictions, such as cuisine type, services o↵ered, ambience, noise level, average rating, etc. Our recommendation system for individual users achieved 72% average prediction...

A ranking r-constraint satisfaction problem (ranking r-CSP for short) consists of a ground set of vertices V , an arity r > 2, a parameter k ∈ N and a constraint system c, where c is a function which maps rankings of r-sized sets S ⊆ V to {0, 1}. The objective is to decide if there exists a ranking σ of the vertices satisfying all but at most k constraints (i.e. ∑ S⊆V,|S|=r c(σ(S)) 6 k). Famous...

This paper presents theoretical analysis on the generalization ability of listwise learning-to-rank algorithms using Rademacher Average. The paper first proposes a theoretical framework for ranking and then proves a theorem which gives a generalization bound to a listwise ranking algorithm based on Rademacher Average of the class of compound functions operating on the corresponding listwise los...