Dynamic Personalized Recommendation Algorithm on Sparse Data
|B.Prasanth1 and R.Latha2
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Recommender systems suggest people items or services of their interest and proved to be an important solution to information overload problem. The big problem of collaborative filtering is its. In order to solve scalability problem, we can implement the Collaborative Filtering algorithm on the cloud computing platform. Recommendation systems are very important in the fields of E-commerce and other Web-based services. One of the main difficulties is dynamically providing high-quality recommendation on sparse data. In this paper, a novel dynamic personalized recommendation algorithm is proposed, in which information contained in both ratings and profile contents are utilized by exploring latent relations between ratings, a set of dynamic features are designed to describe user preferences in multiple phases, and finally a recommendation is made by adaptively weighting the features. Experimental results on public datasets show that the proposed algorithm has satisfying performance.