Bimodal Cross-Validation Approach for Recommender Systems Diagnostics

Bimodal Cross-Validation Approach for Recommender Systems Diagnostics

Dmitry I. Ignatov (National Research University Higher School of Economics, Russia) and Jonas Poelmans (Katholieke Universiteit Leuven, Belgium)
DOI: 10.4018/978-1-4666-1900-5.ch008
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Abstract

Recommender systems are becoming an inseparable part of many modern Internet web sites and web shops. The quality of recommendations made may significantly influence the browsing experience of the user and revenues made by web site owners. Developers can choose between a variety of recommender algorithms; unfortunately no general scheme exists for evaluation of their recall and precision. In this chapter, the authors propose a method based on cross-validation for diagnosing the strengths and weaknesses of recommender algorithms. The method not only splits initial data into a training and test subsets, but also splits the attribute set into a hidden and visible part. Experiments were performed on a user-based and item-based recommender algorithm. These algorithms were applied to the MovieLens dataset, and the authors found classical user-based methods perform better in terms of recall and precision.
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Recommender Algorithms

In this paper without loss of generality we consider only two groups of recommender techniques, which can be called the classical ones, mainly user-based and item-based approaches Badrul et al. (2000), Deshpande et al. (2004). A key notion for these techniques is similarity, which can be expressed as Jacquard measure, Pearson correlation coefficient, cosine similarity etc. Initial data are usually represented by an object-attribute matrix, where the rows describe objects (users) and the columns represent attributes (items). A particular cell of the matrix can be either 1 or 0, which stands for the fact that the item was purchased or not respectively. Also the values can be rates or marks of items, for example, film's rates given by users.

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