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An Item-Oriented Algorithm on Cold-Start Problem in Recommendation System
International Journal of Computer Applications
Recommending new items is an important, yet challenging problem due to the lack of preference history for the new items. To handle this problem, the existing system uses the popular core techniques like collaborative filtering, content-based filtering and combinations of these. In this paper, we propose a market-based approach for seeding recommendations for new items in which new items will reach the audience quickest. To support this approach we purposed the algorithm that match the new itemdoi:10.5120/20380-2606 fatcat:oo3uy77bgveczl5hjocljtpsqe