Semi-supervised tag recommendation - using untagged resources to mitigate cold-start problems

Authors:
Christine Preisach;Leandro Balby Marinho;Lars Schmidt-Thieme
Affiliations:
Information Systems and Machine Learning Lab, University of Hildesheim, Germany;Information Systems and Machine Learning Lab, University of Hildesheim, Germany;Information Systems and Machine Learning Lab, University of Hildesheim, Germany
Venue:
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Year:
2010

Citing 8
Cited 2

Enhanced hypertext categorization using hyperlinks

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Why collective inference improves relational classification

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Relational Ensemble Classification

ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Tied boltzmann machines for cold start recommendations

Proceedings of the 2008 ACM conference on Recommender systems
Tag recommendations in social bookmarking systems

AI Communications
Learning optimal ranking with tensor factorization for tag recommendation

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Pairwise interaction tensor factorization for personalized tag recommendation

Proceedings of the third ACM international conference on Web search and data mining
Information retrieval in folksonomies: search and ranking

ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications

Adaptive neuro-fuzzy pedagogical recommender

Expert Systems with Applications: An International Journal
Measuring and addressing the impact of cold start on associative tag recommenders

Proceedings of the 19th Brazilian symposium on Multimedia and the web

Quantified Score

Hi-index	0.00

Visualization

Abstract

Tag recommender systems are often used in social tagging systems, a popular family of Web 2.0 applications, to assist users in the tagging process. But in cold-start situations i.e., when new users or resources enter the system, state-of-the-art tag recommender systems perform poorly and are not always able to generate recommendations. Many user profiles contain untagged resources, which could provide valuable information especially for cold-start scenarios where tagged data is scarce. The existing methods do not explore this additional information source. In this paper we propose to use a purely graph-based semi-supervised relational approach that uses untagged posts for addressing the cold-start problem. We conduct experiments on two real-life datasets and show that our approach outperforms the state-of-the-art in many cases.