An Item-Oriented Algorithm on Cold-Start Problem in Recommendation System

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 item specification (features) with the existing item and identify whether these features are available in existing item sets or not. The proposed system identifies the user opinion on new item feature those are available in existing item set and generates the quality report of newly launched item (which is not purchased yet).