Proceedings ArticleDOI
Clustering the tagged web
Daniel Ramage,Paul Heymann,Christopher D. Manning,Hector Garcia-Molina +3 more
- pp 54-63
TLDR
It is demonstrated how user-generated tags from large-scale social bookmarking websites such as del.icio.us can be used as a complementary data source to page text and anchor text for improving automatic clustering of web pages.Abstract:
Automatically clustering web pages into semantic groups promises improved search and browsing on the web. In this paper, we demonstrate how user-generated tags from large-scale social bookmarking websites such as del.icio.us can be used as a complementary data source to page text and anchor text for improving automatic clustering of web pages. This paper explores the use of tags in 1) K-means clustering in an extended vector space model that includes tags as well as page text and 2) a novel generative clustering algorithm based on latent Dirichlet allocation that jointly models text and tags. We evaluate the models by comparing their output to an established web directory. We find that the naive inclusion of tagging data improves cluster quality versus page text alone, but a more principled inclusion can substantially improve the quality of all models with a statistically significant absolute F-score increase of 4%. The generative model outperforms K-means with another 8% F-score increase.read more
Citations
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Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora
TL;DR: Labeled LDA is introduced, a topic model that constrains Latent Dirichlet Allocation by defining a one-to-one correspondence between LDA's latent topics and user tags that allows Labeled LDA to directly learn word-tag correspondences.
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#TwitterSearch: a comparison of microblog search and web search
TL;DR: This paper explores search behavior on the popular microblogging/social networking site Twitter and observes that people search Twitter to find temporally relevant information and information related to people, and the results returned from the different corpora support these different uses.
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Survey on social tagging techniques
TL;DR: Different techniques employed to study various aspects of tagging are summarized, including properties of tag streams, tagging models, tag semantics, generating recommendations using tags, visualizations of tags, applications of tags and problems associated with tagging usage.
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Improving Recommender Systems by Incorporating Social Contextual Information
TL;DR: This article proposes a factor analysis approach based on probabilistic matrix factorization to alleviate the data sparsity and poor prediction accuracy problems by incorporating social contextual information, such as social networks and social tags.
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Modeling Interestingness with Deep Neural Networks
TL;DR: In this article, the authors use deep neural networks to learn deep semantic models (DSM) of "interestingness" in click transitions between source and target documents derived from web browser logs, which can be used for contextual entity search, automatic text highlighting, prefetching documents of likely interest, automated content recommendation, automated advertisement placement, etc.
References
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