Constructing a Hierarchical User Interest Structure based on User Profiles

TL;DR

This paper constructs a detailed hierarchical user interest structure from massive user profiles, revealing topic-based clusters that enhance personalized search and recommendation systems.

Contribution

It introduces a novel method to build a multi-level user interest hierarchy from user profiles using clustering algorithms, differing from traditional document-based approaches.

Findings

Created a 26-level hierarchy with 34,676 clusters

Discovered interest clusters are topic-based, not hyponymy-based

Captured concept relativity through cluster labeling

Abstract

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.