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Recommender Systems (RSs) are often assumed to present items to users for one reason – to recommend items a user will likely be interested in. Of course RSs do recommend, but this assumption is biased, with no help of the title, towards the “recommending” the system will do. There is another reason for presenting an item to the user: to learn more about(More)
The quality of the ranking function is an important factor that determines the quality of the Information Retrieval system. Each document is assigned a score by the ranking function; the score indicates the likelihood of relevance of the document given a query. In the vector space model, the ranking function is defined by a mathematic expression such as: ∑(More)
The accuracy of collaborative-filtering recommender systems largely depends on three factors: the quality of the rating prediction algorithm, and the quantity and quality of available ratings. While research in the field of recommender systems often concentrates on improving prediction algorithms, even the best algorithms will fail if they are fed(More)
In this paper, we address the task of active learning for linear regression models in collaborative settings. The goal of active learning is to select training points that would allow accurate prediction of test output values. We propose a new active learning criterion that is aimed at directly improving the accuracy of the output value estimation by(More)
In Collaborative Filtering Recommender Systems user’s preferences are expressed in terms of rated items and each rating allows to improve system prediction accuracy. However, not all of the ratings bring the same amount of information about the user’s tastes. Active Learning aims at identifying rating data that better reflects users’ preferences. Active(More)
This paper introduces the use of social network analysis for socially constructed data to study inter-organizational systems of innovation and their value-add supply chain. Through social network analysis, we explore the structure of relationships among Chinese technology-based companies, foreign technologybased companies with Chinese locations, Chinese(More)