Wikipedia - the online encyclopedia - has long been used as a source of information for researchers, as well as being a subject of research itself. Wikipedia has been shown to be effective in recommender systems, sentiment analysis, validation and multiple domains in information retrieval. One of the reasons for Wikipedia's popularity among researchers and practitioners is the multiple types of information it contains, which enables practitioners to select the right "tool" for their respective tasks. In addition to its great potential, this multitude of information sources also poses a challenge: which sources of information are best suited for a specific problem and how can different types of data be combined? This tutorial aims to provide a holistic view of Wikipedia's different features - text, links, categories, page views, editing history etc. - and explore the different ways they can be utilized in a machine learning framework. By presenting and contrasting the latest works that utilize Wikipedia in multiple domains, this tutorial aims to increase the awareness among researchers and practitioners in these fields to the benefits of utilizing Wikipedia in their respective domains, in particular to the use of multiple sources of information simultaneously.

Wikipedia, like other encyclopedias, includes biographies of notable people. However, because it is jointly written by many contributors, it is subject to constant manipulation by contributors attempting to add biographies of non‐notable people. Over time, Wikipedia has developed inclusion criteria for notable people (e.g., receiving a significant award) based on which newly contributed biographies are evaluated. In this paper we present and analyze a set of simple indicators that can be used to predict which article will eventually be accepted. These indicators do not refer to the content itself, but to meta‐content features (such as the number of categories that the biography is associated with) and to author‐based features (such as if it is a first‐time author). By training a classifier on these features, we successfully reached a high predictive performance (area under the receiver operating characteristic [ROC] curve [AUC] of 0.97) even though we overlooked the actual biography text.

Decision trees have three main disadvantages: reduced performance when the training set is small, rigid decision criteria and the fact that a single "uncharacteristic" attribute might "derail" the classification process. In this paper we present ConfDTree - a post-processing method which enables decision trees to better classify outlier instances. This method, which can be applied on any decision trees algorithm, uses confidence intervals in order to identify these hard-to-classify instances and proposes alternative routes. The experimental study indicates that the proposed post-processing method consistently and significantly improves the predictive performance of decision trees, particularly for small, imbalanced or multi-class datasets in which an average improvement of 5%-9% in the AUC performance is reported.

One important challenge in the field of recommender systems is the sparsity of available data. This problem limits the ability of recommender systems to provide accurate predictions of user ratings. We overcome this problem by using the publicly available user generated information contained in Wikipedia. We identify similarities between items by mapping them to Wikipedia pages and finding similarities in the text and commonalities in the links and categories of each page. These similarities can be used in the recommendation process and improve ranking predictions. We find that this method is most effective in cases where ratings are extremely sparse or nonexistent. Preliminary experimental results on the MovieLens dataset are encouraging.