There is much empirical evidence that item-item collaborative filtering works well in practice. Motivated to understand this, we provide a framework to design and analyze various recommendation algorithms. The setup amounts to online binary matrix completion, where at each time a random user requests a recommendation and the algorithm chooses an entry to reveal in the user’s row. The goal is to...

R ecommendation algorithms are best known for their use on e-commerce Web sites,1 where they use input about a customer’s interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite...

Online music services have tens of millions of tracks. The content itself is broad and covers various musical genres as well as non-musical audio content such as radio plays and podcasts. The sheer scale and diversity of content makes it difficult for a user to find relevant tracks. Relevant recommendations are therefore crucial for a good user experience. Here we present a method to compute tr...

Objective The growing popularity of Automatic Item Generation (AIG) can be attributed to the increasing demand for the production of large pools of operational test items. AIG is an algorithmic way of generating assessment tasks which combines cognitive theories, psychometric practices and computer technologies. The outcome of this algorithmic transcriptions of assessment task is 1986). The ite...

Prior research has yielded mixed results regarding what contributes to psychometrically sound multiple-choice (MC) items. The purpose of the present study was, therefore, twofold: (a) to compare 3-, 4-, and 5-option multiple-choice (MC) tests in terms of psychometric characteristics, and (b) to investigate the relationships between three MC tests and five personality traits. To that end, 150 st...