Various processes related to the task of Information Retrieval (IR) can be interpreted in the context of Multi Criteria Decision Making. For example, the assessment of the relevance of a document (an alternative) to a query can be seen as the process of evaluating the performance of several relevance dimensions (criteria) like topicality, novelty, recency, etc. The same applies to some tasks related to the analysis of user generated content in Social Media (SM); an example is offered by the assessment of the credibility of an online review (alternative), which is based on several features (criteria). What is particularly interesting in this interpretation is the role of aggregation operators, which, for a given alternative, reduce the performace-scores of the considered criteria into a global performace-score of the alternative. In fact, depending on the selected aggregation strategy, different behaviors can be modeled for the considered process. These behaviors can be more intuitively captured by guiding the aggregation by means of fuzzy quantifiers (quantifiers guided aggregation).

In this course, after introducing the main concepts related to IR and to SM analytics, the potential and the impact of aggregation will be shown. It will be also shown that quantifier guided aggregation offers an interesting alternative to the application of machine learning techniques (in particular classifiers).