Abstract. Collaborative filtering aims at helping users find items they should appreciate from huge catalogues. In that field, we can distinguish user-based, item-based and model-based approaches. For each of them, many options play a crucial role for their performances, and in particular the similarity function defined between users or items, the number of neighbors considered for user-or item-based approaches, the number of clusters for model-based approaches using clustering, and the prediction function used. In this paper, we review the main collaborative filtering methods proposed in the litterature and compare them on the same widely used real dataset called MovieLens, and using the same widely used performance measure called Mean Absolute Error (MAE). This study thus allows us to highlight the advantages and drawbacks of each approach, and to propose some default options that we think should be used when using a given approach or designing a new one.

While real data often comes in mixed format, discrete and continuous, many supervised induction algorithms require discrete data. Efficient discretization of continuous attributes is an important problem that has effects on speed, accuracy and understandability of the induction models. In this paper, we propose a new discretization method MODL 1 , founded on a Bayesian approach. We introduce a space of discretization models and a prior distribution defined on this model space. This results in the definition of a Bayes optimal evaluation criterion of discretizations. We then propose a new super-linear optimization algorithm that manages to find near-optimal discretizations. Extensive comparative experiments both on real and synthetic data demonstrate the high inductive performances obtained by the new discretization method.

The ChaLearn AutoML Challenge (The authors are in alphabetical order of last name, except the first author who did most of the writing and the second author who produced most of the numerical analyses and plots.) (NIPS 2015-ICML 2016) consisted of six rounds of a machine learning competition of progressive difficulty, subject to limited computational resources. It was followed by

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Abstract. In supervised machine learning, some algorithms are restricted to discrete data and have to discretize continuous attributes. Many discretization methods, based on statistical criteria, information content, or other specialized criteria, have been studied in the past. In this paper, we propose the discretization method Khiops, 1 based on the chi-square statistic. In contrast with related methods ChiMerge and ChiSplit, this method optimizes the chisquare criterion in a global manner on the whole discretization domain and does not require any stopping criterion. A theoretical study followed by experiments demonstrates the robustness and the good predictive performance of the method.

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Abstract-This paper introduces a novel technique to track structures in time evolving graphs. The method is based on a parameter free approach for three-dimensional co-clustering of the source vertices, the target vertices and the time. All these features are simultaneously segmented in order to build time segments and clusters of vertices whose edge distributions are similar and evolve in the same way over the time segments. The main novelty of this approach lies in that the time segments are directly inferred from the evolution of the edge distribution between the vertices, thus not requiring the user to make an a priori discretization. Experiments conducted on a synthetic dataset illustrate the good behaviour of the technique, and a study of a real-life dataset shows the potential of the proposed approach for exploratory data analysis.