Framework for recommendation on large scale web graphs using Heat Diffusion

Abstract

As the exponential explosion of various contents generated on the Web, Recommendation techniques have become increasingly indispensable. Innumerable different kinds of recommendations are made on the Web every day, including music, images, books recommendations, query suggestions, etc. No matter what types of data sources are used for the recommendations, essentially these data sources can be modeled in the form of graphs. In this paper, aiming at providing a general framework on mining Web graphs for recommendations, • We first propose a novel diffusion method which propagates similarities between different recommendations ; • Then we illustrate how to generalize different recommendation problems into our graph diffusion framework. The proposed framework can be utilized in many recommendation tasks on the World Wide Web, including query suggestions, image recommendations, etc. The experimental analysis on large datasets shows the promising future of our work.