Mining Web
Graphs for Recommendations
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 movies, music, images, books recommendations, query suggestions, tags
recommendations, etc. No matter what types of data sources are used for the
recommendations, essentially these data sources can be modeled in the form of
various types of graphs. In this paper, aiming at providing a general framework
on mining Web graphs for recommendations, (1) we first propose a novel
diffusion method which propagates similarities between different nodes and
generates recommendations; (2) 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, tag recommendations, expert finding, image
recommendations, image annotations, etc. The experimental analysis on large
data sets shows the promising future of our work.
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