NUNI (New User and New Item) Problem for SRSs Using Content Aware Multimedia-Based Approach

Recommendation systems suggest items and users of interest based on preferences of items or users and item or user attributes. In social media-based services of dynamic content (such as news, blog, video, movies, books, etc.), recommender systems face the problem of discovering new items, new users, and both, a problem known as a cold start problem, i.e., the incapability to provide recommendation for new items, new users, or both, due to few rating factors available in the rating matrices. To this end, we present a biclustering technique, a novel cold start recommendation method that solves the problem of identifying the new items and new users, to alleviate the dimensionality of the item-user rating matrix using biclustering technique. To overcome the information exiguity and rating diversity, it uses the smoothing and fusion technique. As discussed, the system presents content aware multimedia-based social recommender media substance from item and user bunches.