Topical Generalization for Presentation of User Profiles

TL;DR

This paper introduces a semantic layout mode for user profiles that groups related topics using Wikipedia categories, improving user understanding and preference over flat profiles.

Contribution

It proposes a novel topical generalization method for organizing user profile topics and compares nested and flat layout modes through user studies.

Findings

Users prefer nested profile structures over flat ones.

Users tend to overlook specific lower-level topics in nested profiles.

A new layout mode is proposed to enhance topic visibility.

Abstract

Fine-grained user profile generation approaches have made it increasingly feasible to display on a profile page in which topics a user has expertise or interest. Earlier work on topical user profiling has been directed at enhancing search and personalization functionality, but making such profiles useful for human consumption presents new challenges. With this work, we have taken a first step toward a semantic layout mode for topical user profiles. We have developed a topical generalization approach which finds coherent groups of topics and adds labels to them, based on their association with broader topics in the Wikipedia category graph. A nested layout mode, employing topical generalization, is compared with a simpler flat layout mode in our user study. The results indicate that users favor the nested structure over flat profiles, but tend to overlook the specific topics on the lower level. We propose a third layout mode to address this issue.