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Luong Vuong Nguyen is a PhD Student in Chung-Ang University, Korea since August 2018. He received the B.S. in Department of Mathematics from Da Nang University of Education and Science, Vietnam in July 2009. And he received M.S. degrees in Department of Computer Engineering from The Da Nang University of Technology in Vietnam in April 2013. His research topics include knowledge engineering on data science by using data mining, machine learning, ambient intelligence, and logical reasoning.
Collaborative filtering recommendation systems, which analyze sets of user ratings, have been applied to various domains and have resulted in considerable improvements in the traditional recommendation system. However, they still have problems with data sparsity and cold‐start of the user ratings. To solve these problems, we present a hybrid recommendation approach by combining collaborative filtering methods and word embedding‐based content analysis. This study focuses on the movie domain, and therefore, the contents of the items are represented as a set of features such as titles, genres, directors, actors, and plots. The main aim of this paper is to understand the content of the movie plot using a word embedding to improve the measurement of similarity of each plot content to other plot content (called plot embedding). To enhance the accuracy in measuring the similarity between movies, we also consider other features such as titles, genres, directors, and actors extracted from movies. In the experiments, the movie dataset was collected by our crowdsourcing platform, which is the OMS platform. The experimental findings indicate that the proposed approach can enhance the efficiency of applied collaborative filtering recommendation systems.
Luong Vuong Nguyen; Tri‐Hai Nguyen; Jason J. Jung; David Camacho. Extending collaborative filtering recommendation using word embedding: A hybrid approach. Concurrency and Computation: Practice and Experience 2021, e6232 .
AMA StyleLuong Vuong Nguyen, Tri‐Hai Nguyen, Jason J. Jung, David Camacho. Extending collaborative filtering recommendation using word embedding: A hybrid approach. Concurrency and Computation: Practice and Experience. 2021; ():e6232.
Chicago/Turabian StyleLuong Vuong Nguyen; Tri‐Hai Nguyen; Jason J. Jung; David Camacho. 2021. "Extending collaborative filtering recommendation using word embedding: A hybrid approach." Concurrency and Computation: Practice and Experience , no. : e6232.
This paper presents a cross-cultural crowdsourcing platform, called OurPlaces, where people from different cultures can share their spatial experiences. We built a three-layered architecture composed of: (i) places (locations where people have visited); (ii) cognition (how people have experienced these places); and (iii) users (those who have visited these places). Notably, cognition is represented as a paring of two similar places from different cultures (e.g., Versailles and Gyeongbokgung in France and Korea, respectively). As a case study, we applied the OurPlaces platform to a cross-cultural tourism recommendation system and conducted a simulation using a dataset collected from TripAdvisor. The tourist places were classified into four types (i.e., hotels, restaurants, shopping malls, and attractions). In addition, user feedback (e.g., ratings, rankings, and reviews) from various nationalities (assumed to be equivalent to cultures) was exploited to measure the similarities between tourism places and to generate a cognition layer on the platform. To demonstrate the effectiveness of the OurPlaces-based system, we compared it with a Pearson correlation-based system as a baseline. The experimental results show that the proposed system outperforms the baseline by 2.5% and 4.1% in the best case in terms of MAE and RMSE, respectively.
Luong Vuong Nguyen; Jason J. Jung; Myunggwon Hwang. OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services. ISPRS International Journal of Geo-Information 2020, 9, 711 .
AMA StyleLuong Vuong Nguyen, Jason J. Jung, Myunggwon Hwang. OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services. ISPRS International Journal of Geo-Information. 2020; 9 (12):711.
Chicago/Turabian StyleLuong Vuong Nguyen; Jason J. Jung; Myunggwon Hwang. 2020. "OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services." ISPRS International Journal of Geo-Information 9, no. 12: 711.
Sustainable energy development consists of design, planning, and control optimization problems that are typically complex and computationally challenging for traditional optimization approaches. However, with developments in artificial intelligence, bio-inspired algorithms mimicking the concepts of biological evolution in nature and collective behaviors in societies of agents have recently become popular and shown potential success for these issues. Therefore, we investigate the latest research on bio-inspired approaches for smart energy management systems in smart homes, smart buildings, and smart grids in this paper. In particular, we give an overview of the well-known and emerging bio-inspired algorithms, including evolutionary-based and swarm-based optimization methods. Then, state-of-the-art studies using bio-inspired techniques for smart energy management systems are presented. Lastly, open challenges and future directions are also addressed to improve research in this field.
Tri-Hai Nguyen; Luong Nguyen; Jason Jung; Israel Agbehadji; Samuel Frimpong; Richard Millham. Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges. Sustainability 2020, 12, 8495 .
AMA StyleTri-Hai Nguyen, Luong Nguyen, Jason Jung, Israel Agbehadji, Samuel Frimpong, Richard Millham. Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges. Sustainability. 2020; 12 (20):8495.
Chicago/Turabian StyleTri-Hai Nguyen; Luong Nguyen; Jason Jung; Israel Agbehadji; Samuel Frimpong; Richard Millham. 2020. "Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges." Sustainability 12, no. 20: 8495.
This paper provides a new approach that improves collaborative filtering results in recommendation systems. In particular, we aim to ensure the reliability of the data set collected which is to collect the cognition about the item similarity from the users. Hence, in this work, we collect the cognitive similarity of the user about similar movies. Besides, we introduce a three-layered architecture that consists of the network between the items (item layer), the network between the cognitive similarity of users (cognition layer) and the network between users occurring in their cognitive similarity (user layer). For instance, the similarity in the cognitive network can be extracted from a similarity measure on the item network. In order to evaluate our method, we conducted experiments in the movie domain. In addition, for better performance evaluation, we use the F-measure that is a combination of two criteria P r e c i s i o n and R e c a l l . Compared with the Pearson Correlation, our method more accurate and achieves improvement over the baseline 11.1% in the best case. The result shows that our method achieved consistent improvement of 1.8% to 3.2% for various neighborhood sizes in MAE calculation, and from 2.0% to 4.1% in RMSE calculation. This indicates that our method improves recommendation performance.
Luong Vuong Nguyen; Min-Sung Hong; Jason J. Jung; Bong-Soo Sohn. Cognitive Similarity-Based Collaborative Filtering Recommendation System. Applied Sciences 2020, 10, 4183 .
AMA StyleLuong Vuong Nguyen, Min-Sung Hong, Jason J. Jung, Bong-Soo Sohn. Cognitive Similarity-Based Collaborative Filtering Recommendation System. Applied Sciences. 2020; 10 (12):4183.
Chicago/Turabian StyleLuong Vuong Nguyen; Min-Sung Hong; Jason J. Jung; Bong-Soo Sohn. 2020. "Cognitive Similarity-Based Collaborative Filtering Recommendation System." Applied Sciences 10, no. 12: 4183.
The aim of this research is to present a crowdsourcing-based recommendation platform called OurMovieSimilarity (OMS), which can collect and sharecognitive feedbacks from users. In particular, we focus on the user’s cognition patterns on the similarity between the two movies. OMS also analyzes the collected data of the user to classify the user group and dynamic changes movie recommendations for each different user. The purpose of this is to make OMS interact intelligently and the data collected faster and more accurately. We received more than a thousand feedbacks from 50 users and did the analyzes this data to group the user. A group of the users can be dynamically changed, with respect to the selection of each user. OMS now still online and collecting data. We have been trying to enrich the cognitive feedback dataset including more than 20,000 feedbacks from 5000 users, so that the recommendation system can make more accurate analysis of user cognitive in choosing the movie similarity.
Nguyen Luong Vuong; Jason J. Jung. Crowdsourcing Platform for Collecting Cognitive Feedbacks from Users: A Case Study on Movie Recommender System. Springer Series in Reliability Engineering 2020, 139 -150.
AMA StyleNguyen Luong Vuong, Jason J. Jung. Crowdsourcing Platform for Collecting Cognitive Feedbacks from Users: A Case Study on Movie Recommender System. Springer Series in Reliability Engineering. 2020; ():139-150.
Chicago/Turabian StyleNguyen Luong Vuong; Jason J. Jung. 2020. "Crowdsourcing Platform for Collecting Cognitive Feedbacks from Users: A Case Study on Movie Recommender System." Springer Series in Reliability Engineering , no. : 139-150.
Nguyen Luong Vuong. Crowdsourcing System for Measuring Cognitive Similarity in Recommendation System. 2021, 1 .
AMA StyleNguyen Luong Vuong. Crowdsourcing System for Measuring Cognitive Similarity in Recommendation System. . 2021; ():1.
Chicago/Turabian StyleNguyen Luong Vuong. 2021. "Crowdsourcing System for Measuring Cognitive Similarity in Recommendation System." , no. : 1.