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Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems, 2009Tagging systems have become major infrastructures on the Web. They allow users to create tags that annotate and categorize content and share them with other users, very helpful in particular for searching multimedia content. However, as tagging is not constrained by a controlled vocabulary and annotation guidelines, tags tend to be noisy and sparse ...
Ralf Krestel +2 more
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Parallel Latent Dirichlet Allocation on GPUs
2018Latent Dirichlet Allocation (LDA) is a statistical technique for topic modeling. Since it is very computationally demanding, its parallelization has garnered considerable interest. In this paper, we systematically analyze the data access patterns for LDA and devise suitable algorithmic adaptations and parallelization strategies for GPUs. Experiments on
Gordon Euhyun Moon +5 more
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The Sensitivity of Latent Dirichlet Allocation for Information Retrieval
Lecture Notes in Computer Science, 2009 European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)It has been shown that the use of topic models for Information retrieval provides an increase in precision when used in the appropriate form.
Laurence A F Park +2 more
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Labeled Phrase Latent Dirichlet Allocation
2016In recent years, topic modeling, such as Latent Dirichlet Allocation (LDA) and its variations, has been widely used to discover the abstract topics in text corpora. There are two state-of-the-art topic models: Labeled LDA (LLDA) and PhraseLDA. LLDA is a supervised generative model which considers the label information, but it does not take into ...
Yi-Kun Tang, Xianling Mao, Heyan Huang
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Latent Dirichlet allocation-based temporal summarization
International Journal of Web Information Systems, 2019PurposeDuring crises such as accidents or disasters, an enormous volume of information is generated on the Web. Both people and decision-makers often need to identify relevant and timely content that can help in understanding what happens and take right decisions, as soon it appears online.
Ahmed Amir Tazibt, Farida Aoughlis
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Tweet Sentiment Analysis with Latent Dirichlet Allocation
International Journal of Information Retrieval Research, 2014The method proposed here analyzes the social sentiments from collected tweets that have at least 1 of 800 sentimental or emotional adjectives. By dealing with tweets posted in a half a day as an input document, the method uses Latent Dirichlet Allocation (LDA) to extract social sentiments, some of which coincide with our daily sentiments. The extracted
Masahiro Ohmura +2 more
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Exploit latent Dirichlet allocation for collaborative filtering
Frontiers of Computer Science, 2018Previous work on the one-class collaborative filtering (OCCF) problem can be roughly categorized into pointwise methods, pairwise methods, and content-based methods. A fundamental assumption of these approaches is that all missing values in the user-item rating matrix are considered negative.
Zhoujun Li 0001 +5 more
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Proceedings of the 2014 ACM conference on Web science, 2014
Topic modeling, in particular the Latent Dirichlet Allocation (LDA) model, has recently emerged as an important tool for understanding large datasets, in particular, user-generated datasets in social studies of the Web. In this work, we investigate the instability of LDA inference, propose a new metric of similarity between topics and a criterion of ...
Sergei Koltsov +2 more
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Topic modeling, in particular the Latent Dirichlet Allocation (LDA) model, has recently emerged as an important tool for understanding large datasets, in particular, user-generated datasets in social studies of the Web. In this work, we investigate the instability of LDA inference, propose a new metric of similarity between topics and a criterion of ...
Sergei Koltsov +2 more
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Joint Latent Dirichlet Allocation for Social Tags
IEEE Transactions on Multimedia, 2018Social tags, serving as a textual source of simple but useful semantic metadata to reflect the user preference or describe the web objects, has been widely used in many applications. However, social tags have several unique characteristics, i.e., sparseness and data coupling (i.e., non-IIDness), which makes existing text analysis methods such as LDA ...
Jiangchao Yao +4 more
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Robust Initialization for Learning Latent Dirichlet Allocation
2015Latent Dirichlet Allocation (LDA) represents perhaps the most famous topic model, employed in many different contexts in Computer Science. The wide success of LDA is due to the effectiveness of this model in dealing with large datasets, the competitive performances obtained on several tasks (e.g. classification, clustering), and the interpretability of
LOVATO, PIETRO +3 more
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