Stacking from Tags: Clustering Bookmarks around a Theme
Arkaitz Zubiaga, Alberto P\'erez Garc\'ia-Plaza, V\'ictor Fresno, and Raquel Mart\'inez

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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplex Network Analysis Techniques · Web Data Mining and Analysis · Spam and Phishing Detection
