Results 21 to 30 of about 1,022,373 (272)
Sensors, 2023 Recommender systems are becoming an integral part of routine life, as they are extensively used in daily decision-making processes such as online shopping for products or services, job references, matchmaking for marriage purposes, and many others ...
Balraj Kumar +4 more
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Continuous Semi-Supervised Nonnegative Matrix Factorization
Algorithms, 2023 Nonnegative matrix factorization can be used to automatically detect topics within a corpus in an unsupervised fashion. The technique amounts to an approximation of a nonnegative matrix as the product of two nonnegative matrices of lower rank. In certain
Michael R. Lindstrom +4 more
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A network approach to topic models [PDF]
Science Advances, 2018 A new approach to topic models finds topics through community detection in word-document networks.
Eduardo G. Altmann +4 more
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Enhancing Indonesian customer complaint analysis: LDA topic modelling with BERT embeddings
Jurnal Ilmiah SINERGI, 2023 Social media data can be mining for recommended systems to know the best trends or patterns. The customers have the freedom to ask questions about the product, tell their demands, and convey their complaints through social media.
Mutiara Auliya Khadija +1 more
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Renormalization Analysis of Topic Models [PDF]
Entropy, 2020 In practice, to build a machine learning model of big data, one needs to tune model parameters. The process of parameter tuning involves extremely time-consuming and computationally expensive grid search. However, the theory of statistical physics provides techniques allowing us to optimize this process. The paper shows that a function of the output of
Sergei Koltcov, Vera Ignatenko
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On a Topic Model for Sentences
Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016 Probabilistic topic models are generative models that describe the content of documents by discovering the latent topics underlying them. However, the structure of the textual input, and for instance the grouping of words in coherent text spans such as sentences, contains much information which is generally lost with these models.
Balikas, Georgios +2 more
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, 2012 We introduce the topical relevance model (TRLM) as a generalization of the standard relevance model (RLM). The TRLM alleviates the limitations of the RLM by exploiting the multi-topical structure of pseudo-relevant documents. In TRLM, intra-topical document and query term co-occurrences are favoured, whereas the inter-topical ones are down-weighted ...
Ganguly, Debasis +2 more
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An Exploratory Study of COVID-19 Information on Twitter in the Greater Region
Big Data and Cognitive Computing, 2021 The outbreak of the COVID-19 led to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information ...
Ninghan Chen, Zhiqiang Zhong, Jun Pang
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Crosslingual Topic Modeling with WikiPDA [PDF]
Proceedings of the Web Conference 2021, 2021 We present Wikipedia-based Polyglot Dirichlet Allocation (WikiPDA), a crosslingual topic model that learns to represent Wikipedia articles written in any language as distributions over a common set of language-independent topics. It leverages the fact that Wikipedia articles link to each other and are mapped to concepts in the Wikidata knowledge base ...
Piccardi, Tiziano, West, Robert
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Semantic Knowledge Discovery for User Profiling for Location-Based Recommender Systems
Human-Centric Intelligent Systems, 2021 This paper introduces a purposed Location-based Recommender System (LBRS) that combines sentiment analysis and topic modelling techniques to improve user profiling for enhancing recommendations of Points of Interest (POIs).
Xiaohui Tao +3 more
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