Preference Relation-based Markov Random Fields for Recommender Systems

Ordinal Random Fields for Recommender Systems

Recommender Systems heavily rely on numerical preferences, whereas the importance of ordinal preferences has only been recognised in recent works of Ordinal Matrix Factorisation (OMF). Although the OMF can effectively exploit ordinal properties, it captures only the higher-order interactions among users and items, without considering the localised interactions properly. This paper employs Marko...

Random Projection-Based Anderson-Darling Test for Random Fields

In this paper, we present the Anderson-Darling (AD) and Kolmogorov-Smirnov (KS) goodness of fit statistics for stationary and non-stationary random fields. Namely, we adopt an easy-to-apply method based on a random projection of a Hilbert-valued random field onto the real line R, and then, applying the well-known AD and KS goodness of fit tests. We conclude this paper by studying the behavior o...

Random Indexing for Content-Based Recommender Systems

The use of Vector Space Models (VSM) in the area of Information Retrieval is an established practice, thanks to its very clean and solid formalism that allows us to easily represent objects in a vector space and to perform calculations on them. The goal of this work is to investigate the impact of VSM on Recommender Systems (RS) performance. Specifically, we will introduce two approaches: the f...

Evaluating Preference-Based Feedback in Recommender Systems

Markov Random Fields and Conditional Random Fields

Markov chains provided us with a way to model 1D objects such as contours probabilistically, in a way that led to nice, tractable computations. We now consider 2D Markov models. These are more powerful, but not as easy to compute with. In addition we will consider two additional issues. First, we will consider adding observations to our models. These observations are conditioned on the value of...