Laurent Candillier

Comparing State-of-the-Art Collaborative Filtering Systems

Collaborative filteringaims at helping usersfind itemsthey should appreciate from huge catalogues. In that field, we can distinguish user-based, item-basedand model-basedapproaches. 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.

Designing Specific Weighted Similarity Measures to Improve Collaborative Filtering Systems

The aim of collaborative filteringis to help usersto find itemsthat they should appreciate from huge catalogues. In that field, we can distinguish user-basedfrom item-basedapproaches. The former is based on the notion of user neighbourhoods while the latter uses item neighbourhoods. The definition of similaritybetween users and items is a key problem in both approaches. While traditional similarity measures can be used, we will see in this paper that bespoke ones, that are tailored to type of data that is typically available (i.e. very sparse), tend to lead to better results. Extensive experiments are conducted on two publicly available datasets, called MovieLensand Netflix. Many similarity measures are compared. And we will show that using weighted similarity measures significantly improves the results of both user- and item-based approaches.

SSC: statistical subspace clustering

Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces within a dataset. This is a particularly important challenge with high dimensional data where the curse of dimensionality occurs. It has also the benefit of providing smaller descriptions of the clusters found. Existing methods only consider numerical databases and do not propose any method for clusters visualization. Besides, they require some input parameters difficult to set for the user. The aim of this paper is to propose a new subspace clustering algorithm, able to tackle databases that may contain continuous as well as discrete attributes, requiring as few user parameters as possible, and producing an interpretable output. We present a method based on the use of the well-known EM algorithm on a probabilistic model designed under some specific hypotheses, allowing us to present the result as a set of rules, each one defined with as few relevant dimensions as possible. Experiments, conducted on artificial as well as real databases, show that our algorithm gives robust results, in terms of classification and interpretability of the output.

Cascade evaluation of clustering algorithms

This paper is about the evaluation of the results of clustering algorithms, and the comparison of such algorithms. We propose a new method based on the enrichment of a set of independent labeled datasets by the results of clustering, and the use of a supervised method to evaluate the interest of adding such new information to the datasets. We thus adapt the cascade generalization [1] paradigm in the case where we combine an unsupervised and a supervised learner. We also consider the case where independent supervised learnings are performed on the different groups of data objects created by the clustering [2]. We then conduct experiments using different supervised algorithms to compare various clustering algorithms. And we thus show that our proposed method exhibits a coherent behavior, pointing out, for example, that the algorithms based on the use of complex probabilistic models outperform algorithms based on the use of simpler models.