  The interests of individual internet users fall into a hierarchical structure
which is useful in regards to building personalized searches and
recommendations. Most studies on this subject construct the interest hierarchy
of a single person from the document perspective. In this study, we constructed
the user interest hierarchy via user profiles. We organized 433,397 user
interests, referred to here as "attentions", into a user attention network
(UAN) from 200 million user profiles; we then applied the Louvain algorithm to
detect hierarchical clusters in these attentions. Finally, a 26-level hierarchy
with 34,676 clusters was obtained. We found that these attention clusters were
aggregated according to certain topics as opposed to the hyponymy-relation
based conceptual ontologies. The topics can be entities or concepts, and the
relations were not restrained by hyponymy. The concept relativity encapsulated
in the user's interest can be captured by labeling the attention clusters with
corresponding concepts.
