Dynamic Personalized Recommendation on Sparse Data

Home / Data mining / Dynamic Personalized Recommendation on Sparse Data
Name Dynamic Personalized Recommendation on Sparse Data
Technology .net
Category Data Mining
Description Recommendation techniques 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.
IEEE Paper Yes
IEEE Paper Year 2013

Contact Form

Previous
Next