IBM Certified Solution Designer (Object Oriented Analysis and Design, vUML 2) Permalink
Issued by IBM, May 2010
Posts by Collection
certifications
Machine Learning on Google Cloud Specialization Permalink
Issued by Google Cloud Training, Oct 2021
End-to-End Machine Learning with TensorFlow on Google Cloud Permalink
Issued by Google Cloud Training, Nov 2021
Production Machine Learning Systems Permalink
Issued by Google Cloud Training, Nov 2021
CA001: Climate Action: Solutions for a Changing Planet Permalink
Issued by SDGAcademy, Apr 2024
publications
User Interest Analysis with Hidden Topic in News Recommendation System
Published in IEEE Computer Society Washington, DC, USA, 2010
To take advantage of the Internet - vast but complicated information resources, Recommendation systems help users find out information they need by providing them personalized suggestions. This research area is receiving more and more attention from researchers and used in some famous websites like EBay, Amazon, etc. In this paper, we proposed a Recommendation System for Vietnamese electronic newspaper which uses content-based filtering techniques associating with the attention of users shown in user’s profile. These users’ attentions are determined by inferring a set of common Hidden Topics from the documents which users preferred. Experimental results showed that approach is feasible with positive results and its capabilities for reality development.
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A Model of Vietnamese Person Named Entity Question Answering System
Published in PACLIC, Bali, Indonesia, 2012
In this paper, we proposed a Vietnamese named entity question answering (QA) model. This model applies an analytical question method using CRF machine learning algorithm combined with two automatic answering strategies: indexed sentences database-based and Google search engine-based. We gathered a Vietnamese question dataset containing about 2000 popular “Who, Whom, Whose” questions to evaluate our question chunking method and QA model. According to experiments, question chunking phase acquired the average F1 score of 92.99%. Equally significant, in our QA evaluation, experimental results illustrated that our approaches were completely reasonable and realistic with 74.63% precision and 87.9% ability to give the answers.
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