Recommender systems are among today's most successful application areas of artificial intelligence. However, in the recommender systems research community, we have fallen prey to a McNamara fallacy to a worrying extent: In the majority of our research efforts, we rely almost exclusively on computational measures such as prediction accuracy, which are easier to make than applying other evaluation methods. However, it remains unclear whether small improvements in terms of such computational measures matter greatly and whether they lead us to better systems in practice. A paradigm shift in terms of our research culture and goals is therefore needed. We can no longer focus exclusively on abstract computational measures but must direct our attention to research questions that are more relevant and have more impact in the real world. In this work, we review the various ways of how recommender systems may create value; how they, positively or negatively, impact consumers, businesses, and the society; and how we can measure the resulting effects.

Measuring the quality of plot is a desirable feature for computational narrative systems.One of the notions of plot quality used in narrative theory is called tellability, which can be derived from certain structural properties, namely the types of events present and the way they are connected.These structures include not only actualized events, but also take into account virtual plans and the affective valencies of events.The present paper introduces Marie-Laure Ryan's tellability principles and suggests to computationally model them using an affective multi-agent simulation system.It discusses how such an approach implies a broader understanding of plot than commonly assumed and analysis several existing narrative systems under these considerations.Furthermore, it introduces a plot-graph formalism that allows the computational representation and analysis of the extended plot understanding.An approach to automatically generating the plot-graph is suggested in the context of the introduced multi-agent simulation system.

Creativity is a crucial aspect of human culture, yet it is hard to define and harder yet to measure. "The essence behind the term is being able to come up with new ways of seeing and doing," HEC researcher Mitali Banerjee explains. Creativity not only defines the work of artistic pioneers or visionary scientists, but its different forms also animate the activities of businesses in industries ranging from technology to entertainment. "In some instances, such as the iPhone, creativity can involve recombining existing technologies in a new design; in other instances, creativity can involve designing new organizational processes," says Mitali Banerjee. Apart from the difficulties of measuring creativity, little is understood about how creativity is valued in our society.

This study evaluates the robustness of established computational indices used to assess text relatedness in user-language. The original User-Language Paraphrase Corpus (ULPC) was compared to a corrected version, in which each paraphrase was corrected for typographical and grammatical errors. Error correction significantly affected values for each of five computational indices, indicating greater similarity of the target sentence to the corrected paraphrase than to the original paraphrase. Moreover, misspelled target words accounted for a large proportion of the differences. This study also evaluated potential effects on correlations between computational indices and human ratings of paraphrases. The corrections did not yield assessments that were any more or less comparable to trained human raters than were the original paraphrases containing typographical or grammatical errors. The results suggest that although correcting for errors may optimize certain computational indices, the corrections are not necessary for comparing the indices to expert ratings.