Matevz Kunaver

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In recent years, research into user centric and personalized applications has focused on the utilization of contextual information about the situation in which the user is consuming the content item. However, there is no database suitable for the investigation of specific open issues of contextual information description and utilization available today. The(More)
This paper presents two different methods for diversifying recommendations that were developed as part of the ESWC2014 challenge. Both methods focus on post-processing recommendations provided by the baseline recommender system and have increased the ILD at the cost of final precision (measured with F@20). The authors feel that this method has potential yet(More)
This paper presents three different methods for diversifying search results, that were developed as part of our user modelling research. All three methods focus on post-processing search results provided by the baseline recommender systems and increase the diversity (measured with ILD@20) at the cost of final precision (measured with F@20). The authors feel(More)
One of the well-known issues with content recommender system is that they tend to become over-specialized, which often has a negative influence on user experience. This can be solved by diversification of the recommendation list, a process that implements a tradeoff between accuracy and diversity of recommended items. Normally, item metadata is used in the(More)
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