Stacking from Tags: Clustering Bookmarks around a Theme

TL;DR

This paper explores automatic clustering of web pages based on tags to identify thematic stacks, aiming to enhance recommendations on social bookmarking platforms like Delicious.

Contribution

It introduces a method for clustering web pages from tags to discover stacks, addressing the gap of unstacked pages and improving recommendation systems.

Findings

Initial clustering approach shows promise in identifying thematic stacks

Many web pages remain unstacked, indicating room for improved clustering methods

Potential for enhanced recommendation accuracy through automated stacking

Abstract

Since very recently, users on the social bookmarking service Delicious can stack web pages in addition to tagging them. Stacking enables users to group web pages around specific themes with the aim of recommending to others. However, users still stack a small subset of what they tag, and thus many web pages remain unstacked. This paper presents early research towards automatically clustering web pages from tags to find stacks and extend recommendations.