ABSTRACT
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.
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