scholarly journals A Regional Topic Model Using Hybrid Stochastic Variational Gibbs Sampling for Real-Time Video Mining

Algorithms ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 97
Author(s):  
Lin Tang ◽  
Lin Liu ◽  
Jianhou Gan
Keyword(s):  
Real Time ◽  
Gibbs Sampling ◽  
Topic Model ◽  
Video Mining

scholarly journals Real-time traffic incident detection using a probabilistic topic model

2015 ◽  
Vol 54 ◽  
pp. 169-188 ◽  
Author(s):  
Akira Kinoshita ◽  
Atsuhiro Takasu ◽  
Jun Adachi
Keyword(s):  
Real Time ◽  
Topic Model ◽  
Incident Detection ◽  
Traffic Incident ◽  
Real Time Traffic ◽  
Probabilistic Topic Model

scholarly journals Optimization of an Intelligent Music-Playing System Based on Network Communication

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liaoyan Zhang
Keyword(s):  
Real Time ◽  
Recommendation System ◽  
Topic Model ◽  
User Interaction ◽  
Interaction Data ◽  
Music Recommendation ◽  
System Testing ◽  
The Real ◽  
Wide Range ◽  
And Performance

Streaming media server is the core system of audio and video application in the Internet; it has a wide range of applications in music recommendation. As song libraries and users of music websites and APPs continue to increase, user interaction data are generated at an increasingly fast rate, making the shortcomings of the original offline recommendation system and the advantages of the real-time streaming recommendation system more and more obvious. This paper describes in detail the working methods and contents of each stage of the real-time streaming music recommendation system, including requirement analysis, overall design, implementation of each module of the system, and system testing and analysis, from a practical scenario. Moreover, this paper analyzes the current research status and deficiencies in the field of music recommendation by analyzing the user interaction data of real music websites. From the actual requirements of the system, the functional and performance goals of the system are proposed to address these deficiencies, and then the functional structure, general architecture, and database model of the system are designed, and how to interact with the server side and the client side is investigated. For the implementation of data collection and statistics module, this paper adopts Flume and Kafka to collect user behavior data and uses Spark Streaming and Redis to count music popularity trends and support efficient query. The recommendation engine module in this paper is designed and optimized using Spark to implement incremental matrix decomposition on data streams, online collaborative topic model, and improved item-based collaborative filtering algorithm. In the system testing section, the functionality and performance of the system are tested, and the recommendation engine is tested with real datasets to show the discovered music themes and analyze the test results in detail.

Hierarchical Interpretable Topical Embeddings for Exploratory Search and Real-Time Document Tracking

2020 ◽  
Vol 11 (4) ◽  
pp. 134-152
Author(s):  
Anastasia Ianina ◽  
Konstantin Vorontsov
Keyword(s):  
Real Time ◽  
Information Acquisition ◽  
Search Task ◽  
Topic Model ◽  
Relevant Information ◽  
Exploratory Search ◽  
Scientific Papers ◽  
Fast Processing ◽  
Low Dimensional ◽  
Regularized Em

Real-time monitoring of scientific papers and technological news requires fast processing of complicated search demands motivated by thematically relevant information acquisition. For this case, the authors develop an exploratory search engine based on probabilistic hierarchical topic modeling. Topic model gives a low dimensional sparse interpretable vector representation (topical embedding) of a text, which is used for ranking documents by their similarity to the query. They explore several ways of comparing topical vectors including searching with thematically homogeneous text segments. Topical hierarchies are built using the regularized EM-algorithm from BigARTM project. The topic-based search achieves better precision and recall than other approaches (TF-IDF, fastText, LSTM, BERT) and even human assessors who spend up to an hour to complete the same search task. They also discover that blending hierarchical topic vectors with neural pretrained embeddings is a promising way of enriching both models that helps to get precision and recall higher than 90%.

The Influence of Process Management: Uncovering the Impact of Real-Time Managerial Interventions via a Topic Modeling Approach

2021 ◽  
pp. 1-46
Author(s):  
Joshua Gyory ◽  
Kenneth Kotovsky ◽  
Jonathan Cagan
Keyword(s):  
Problem Solving ◽  
Real Time ◽  
Process Management ◽  
Topic Model ◽  
Design Team ◽  
Design Cognition ◽  
Team Members ◽  
Conceptual Engineering ◽  
Before And After ◽  
The Impact

Abstract Computationally studying team discourse can provide valuable, real-time insights into the state of design teams and design cognition during problem-solving. The particular experimental design, adopted from previous work by the authors, places one of the design team conditions under the guidance of a human process manager. In that work, teams under this process management outperformed the unmanaged teams in terms of their design performance. This opens the opportunity to not only model design discourse during problem solving, but more critically, to explore process manager interventions and their impact on design cognition. Utilizing this experimental framework, a topic model is trained on the discourse of human designers of both managed and unmanaged teams collaboratively solving a conceptual engineering design task. Results show that the two team conditions significantly differ in a number of the extracted topics, and in particular, those topics that most pertain to the manager interventions. A dynamic look during the design process reveals that the largest differences between the managed and unmanaged teams occur during the latter half of problem-solving. Furthermore, a before and after analysis of the topic-motivated interventions reveals that the process manager interventions significantly shift the topic mixture of the team members’ discourse immediately after intervening. Taken together, these results from this work not only corroborate the effect of the process manager interventions on design team discourse and cognition but provide promise for the computational detection and facilitation of design interventions based on real-time, discourse data.

The Development of Topic Model Based on Beta-Negative Binomial Process

2013 ◽  
Vol 427-429 ◽  
pp. 1597-1600
Author(s):  
Ya Shu Liu ◽  
Han Bing Yan
Keyword(s):  
Data Mining ◽  
Parameter Estimation ◽  
Gibbs Sampling ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Topic Model ◽  
Negative Binomial Process ◽  
Binomial Process ◽  
Design Experiments

. Topic Model is one of the important subfields in Data Mining, which has been developed very quickly and has been applicated in many fields in recent years. Many researchers have been engaged in this field. In this paper, we introduce the BNB process based on Beta and Negative Binomial distribution, using the hierarchical distribution instead of Dirichlet in LDA. And we give the expression of parameter estimation used by Gibbs sampling. Then, BNB process is applicated in the text topic classification. We design experiments to decide the numbers of topics and compare the BNB process with LDA. Experiment results show that the BNB process has better performance over LDA in English Dataset, but they have almost the same result in Chinese micro-blog topic classification. Finally we analyze the problem and give the idea in further research.

Two time-efficient gibbs sampling inference algorithms for biterm topic model

2017 ◽  
Vol 48 (3) ◽  
pp. 730-754 ◽  
Author(s):  
Xiaotang Zhou ◽  
Jihong Ouyang ◽  
Ximing Li
Keyword(s):  
Gibbs Sampling ◽  
Topic Model ◽  
Inference Algorithms
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