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Personalized search, navigation and content delivery techniques have attracted interest in the recommender systems as a means to decrease search ambiguity and return results most relevant to a particular user preferences. In this paper, we study the effect of incorporating user semantic profile derived from past user's behavior and preferences on the accuracy of a recommender system. We present a...
Many e-commerce web sites such as online book retailers or specialized information hubs such as online movie databases make use of recommendation systems where users are directed to items of interests based on past user interactions. While keyword based approaches are naive and do not take content or context into account, collaborative filtering and content-based techniques suffer from biased ratings,...
Recommender systems have changed the way we live for they provide strategies that help users search or make decisions within the overwhelming information spaces nowadays. They have played an important role in some area such as e-commerce and e-learning. Most of the recommender systems suffer from different weaknesses introduced by different technique, such as the New Item Problem, the Rating Sparsity...
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