Browsing by Author "Dumbleton, Bronwyn Catherine"

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    Recommender systems
    (Stellenbosch : Stellenbosch University, 2019-12) Dumbleton, Bronwyn Catherine; Bierman, Surette; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science.
    ENGLISH ABSTRACT: A Recommender System (RS) is a particular type of information filtering system used to propose relevant items to users. Their successful application in online retail is reflected in increased customer satisfaction and sales revenue, with further application in entertainment, e-commerce and services, and content. Hence it may be argued that recommender systems currently present some of the most successful and widely used machine learning algorithms in practice. We provide an overview of both standard and more modern approaches to recommender systems, including content-based and collaborative filtering, as well as latent factor models for collaborative filtering. A limitation of standard latent factor models is that their input is typically restricted to a set of item ratings. In contrast, general purpose supervised learning algorithms allow more flexible inputs, but are typically not able to handle the degree of data sparsity prevalent in recommendation problems. Factorisation machines, which are supervised learning methods, are able to incorporate more flexible inputs and are well suited to deal with the effects of data sparsity. We therefore study the use of factorisation in recommender problems and report an empirical study in which we compare the effects of data sparsity on latent factor models, as well as on factorisation machines. Currently in RS research, emphasis is placed on the advantages of recommender systems that yield recommendations that are simple to explain to users. Such recommender systems have been shown to be much more trustworthy than more complex, unexplainable systems. Towards a proposal for explainable recommendations, we also provide an overview of the connection between the recommender problem and Multi-Label Classification (MLC). Since some of the recent MLC approaches facilitate the interpretation of predictions, we conduct an empirical study in order to evaluate the use of various MLC approaches in the context of recommender problems.