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In major e-commerce recommendation systems, the number of users and items is very large and available data are insufficient for identifying similar users. As a result, recommender systems could not use users' opinion to make suggestions to other users and the quality of the recommendations might reduce. The main objective of the authors' research is to provide high quality recommendations even when sufficient data are unavailable. In this paper, they have presented a model for this condition that combines recommendation methods (e.g., Collaborative Filtering (CF) and Content Based Filtering (CBF)) with other methods such as clustering and association rules.
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