Learning User Profiles from Tagging Data and Leveraging them for Personal(ized) Information Access

TLDR

Social bookmarking systems generate abundant metadata, and aggregating a user's tags yields a profile akin to those used in information filtering. The study aims to construct user profiles from tagging data and investigate how to leverage them for personalized information access. The Add‑A‑Tag algorithm builds profiles by incorporating the structural and temporal aspects of tagging data. The study found that users value aggregated tagging histories and that profiles can guide navigation to provide personalized access to information resources.

Abstract

Due to the high popularity of social bookmarking systems, a large amount of metadata is available. Aggregating the metadata belonging to one user results in an user profile similar to those often used in Information Filtering. This paper shows how to create user profiles from tagging data. We present the Add-A-Tag algorithm for profile construction which takes account of the structural and temporal nature of tagging data. In addition, we explore ways of leveraging these user profiles. There are two main insights gained. Firstly, as we experienced in a small-scale user study, simply being able to view aggregated information about past tagging behavior was considered useful. Secondly, the user profile can be used to guide the user’s navigation, that is, to provide the user with personalized access to information resources.