Latent dirichlet allocation for tag recommendation

Krestel, Ralf; Fankhauser, Péter; Nejdl, Wolfgang

doi:10.1145/1639714.1639726

Cited by 385 publications

(230 citation statements)

References 26 publications

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“…Due to the usefulness of tag recommendation, many methods have been proposed from different perspectives (Heymann et al, 2008;Krestel et al, 2009;Rendle et al, 2009;Liu et al, 2012;Ding et al, 2013). Heymann et al (Heymann et al, 2008) investigated the tag recommendation problem using the data collected from social bookmarking system.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, the task of recommending hashtags for microblogs has become an important research topic and has received considerable attention in recent years. Existing works have studied discriminative models (Ohkura et al, 2006;Heymann et al, 2008) and generative models (Blei and Jordan, 2003;Krestel et al, 2009;Ding et al, 2013;Godin et al, 2013) based on the textual information of a single microblog.…”

Section: Introductionmentioning

confidence: 99%

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Hashtag Recommendation Using Dirichlet Process Mixture Models Incorporating Types of Hashtags

Gong

Zhang

Huang

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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In recent years, the task of recommending hashtags for microblogs has been given increasing attention. Various methods have been proposed to study the problem from different aspects. However, most of the recent studies have not considered the differences in the types or uses of hashtags. In this paper, we introduce a novel nonparametric Bayesian method for this task. Based on the Dirichlet Process Mixture Models (DPMM), we incorporate the type of hashtag as a hidden variable. The results of experiments on the data collected from a real world microblogging service demonstrate that the proposed method outperforms stateof-the-art methods that do not consider these aspects. By taking these aspects into consideration, the relative improvement of the proposed method over the state-of-theart methods is around 12.2% in F1-score.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hashtag Recommendation Using Dirichlet Process Mixture Models Incorporating Types of Hashtags

Gong

Zhang

Huang

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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“…At present, the LDA model has been used in many text-relevant fields, such as text classification and information retrieval [11][12][13].…”

Section: Topic Relevancementioning

confidence: 99%

Learning to rank based gene summary extraction

Shang

Hao

Lin

et al. 2013

2013 IEEE International Conference on Bioinformatics and Biomedicine

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Background: In recent years, the biomedical literature has been growing rapidly. These articles provide a large amount of information about proteins, genes and their interactions. Reading such a huge amount of literature is a tedious task for researchers to gain knowledge about a gene. As a result, it is significant for biomedical researchers to have a quick understanding of the query concept by integrating its relevant resources. Methods: In the task of gene summary generation, we regard automatic summary as a ranking problem and apply the method of learning to rank to automatically solve this problem. This paper uses three features as a basis for sentence selection: gene ontology relevance, topic relevance and TextRank. From there, we obtain the feature weight vector using the learning to rank algorithm and predict the scores of candidate summary sentences and obtain top sentences to generate the summary. Results: ROUGE (a toolkit for summarization of automatic evaluation) was used to evaluate the summarization result and the experimental results showed that our method outperforms the baseline techniques.

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“…Latent Dirichlet Allocation (LDA) [15] is one such generative probabilistic model used over discrete data such as text corpora. LDA has been applied to many tasks such as word sense disambiguation [16], named entity recognition [17], tag recommendation [18], community recommendation [19], etc. In this work, we apply LDA on user profile data with the goal of producing a reduced set of features that capture user interests and improve the accuracy of the link prediction task in social networks.…”

Section: Related Workmentioning

confidence: 99%

Predicting Friendship Links in Social Networks Using a Topic Modeling Approach

Parimi

Caragea

2011

Lecture Notes in Computer Science

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Abstract. In the recent years, the number of social network users has increased dramatically. The resulting amount of data associated with users of social networks has created great opportunities for data mining problems. One data mining problem of interest for social networks is the friendship link prediction problem. Intuitively, a friendship link between two users can be predicted based on their common friends and interests. However, using user interests directly can be challenging, given the large number of possible interests. In the past, approaches that make use of an explicit user interest ontology have been proposed to tackle this problem, but the construction of the ontology proved to be computationally expensive and the resulting ontology was not very useful. As an alternative, we propose a topic modeling approach to the problem of predicting new friendships based on interests and existing friendships. Specifically, we use Latent Dirichlet Allocation (LDA) to model user interests and, thus, we create an implicit interest ontology. We construct features for the link prediction problem based on the resulting topic distributions. Experimental results on several LiveJournal data sets of varying sizes show the usefulness of the LDA features for predicting friendships.

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Latent dirichlet allocation for tag recommendation

Cited by 385 publications

References 26 publications

Hashtag Recommendation Using Dirichlet Process Mixture Models Incorporating Types of Hashtags

Hashtag Recommendation Using Dirichlet Process Mixture Models Incorporating Types of Hashtags

Learning to rank based gene summary extraction

Predicting Friendship Links in Social Networks Using a Topic Modeling Approach

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