Recommendation Technique for the “Cold-Start” Problem
Journal Title: Revista Romana de Interactiune Om-Calculator - Year 2016, Vol 9, Issue 3
Abstract
Usually, users that benefit from a Recommender System outputs only get a list of items that the system assumes the best match their needs, without having any clue regarding how the system managed to figure out what they like. In this paper, we propose a mechanism that generates content-based recommendations organized on levels of similarities with the selected product in order to let the user decide about what similarity degree (s)he wants to explore. We demonstrate empirically that our proposed mechanism can ensure good performance for a recommendation technique under the cold-start conditions.
Authors and Affiliations
Mihaela Colhon, Adrian Iftene
Keywords
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