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Papers by Nghia Phan Quoc

Research paper thumbnail of A Lasso-based Collaborative Filtering Recommendation Model

International Journal of Advanced Computer Science and Applications, 2022

Research paper thumbnail of Collaborative Filtering Recommendation Based on Statistical Implicative Analysis

Communications in computer and information science, 2020

Research paper thumbnail of Similarity Kernel for User-based Collaborative Filtering Recommendation System

EAI endorsed transactions on context-aware systems and applications, Jul 6, 2017

Research paper thumbnail of Similarity Kernel for User-based Collaborative Filtering Recommendation System

EAI Endorsed Transactions on Context-aware Systems and Applications, 2017

Research paper thumbnail of Interestingnesslab: A Framework for Developing and Using Objective Interestingness Measures

Advances in Intelligent Systems and Computing, 2016

The objective interestingness measures play an important role in data mining because they are use... more The objective interestingness measures play an important role in data mining because they are used for mining, filtering and ranking the patterns. However, there is no research that collects the measures fully as well as there is no tool that can: automatically calculate the interestingness values of the patterns by using those measures, and is the framework for rapidly developing the applications related to objective interestingness measures. This paper describes Interestingnesslab - a tool of the objective interestingness measures is developed in the R language. The main functions of the tool are: mining a set of association rules and presenting them by the cardinalities (\(n,n_{X},n_{Y},n_{X\overline{Y}}\)), calculating the interestingness value of an association rule according to 1 of 109 collected measures; calculating the interestingness values of the whole rule set in many measures selected by the user; discovering the tendencies in a data set and recommending the top N items to the user; and studying the specific behavior of a set of interestingness measures in the context of a specific dataset and in an exploratory data analysis perspective. With Interestingnesslab, the user can easily and quickly reuse its functions to develop his/her own applications.

Research paper thumbnail of Context-Aware Recommendation with Objective Interestingness Measures

Context-aware recommender systems researches now concentrate on adjusting recommendation results ... more Context-aware recommender systems researches now concentrate on adjusting recommendation results for situations specific context of the users. These studies suggest many ways to integrate user contextual information into the recommendation process such as using topic hierarchies with matrix factorization techniques to improve context-aware recommender systems, measuring frequency-based similarity for context-aware recommender systems, collecting data from social networking to support context-aware recommender systems, and so on. However, these studies mainly focus on the development of context-aware recommendation algorithms to propose items to users in a particular situation and do not care about the extent of contextual involvement in the recommendation process to make recommendation results. In this article, we propose a new approach for context-aware recommender systems based on objective interestingness measures to consider the contextual relationship of the users in the recomm...

Research paper thumbnail of Collaborative Recommenderation Based on Statistical Implication Rules

Journal of Computer Science and Cybernetics, 2018

In recent research, many approaches based on association rules have been proposed to improve the ... more In recent research, many approaches based on association rules have been proposed to improve the accuracy of recommender systems. These approaches are primarily based on Apriori data mining algorithm in order to generate the association rules and apply them to improving the recommendation results. However, these approaches also reveal some disadvantages of the system, such as taking a longer time for generating association rules; applying the Apriori algorithm on rating sparse matrix resulting in irrelevant information and causing poor recommendation results to target users and association rules generated primarily relying on given threshold of Support and Confidence measures leading to the focus on the majority of rules and ignoring the astonishment of rules to affect the recommendation results. In this study, we propose a new model for collaborative filtering recommender systems: The collaborative recommendation is based on statistical implication rules (IIR); Different from colla...

Research paper thumbnail of Context-Similarity Collaborative Filtering Recommendation

Research paper thumbnail of Classification of objective interestingness measures

EAI Endorsed Transactions on Context-aware Systems and Applications, 2016

The creation of the interestingness measures for evaluating the quality of the association rule-b... more The creation of the interestingness measures for evaluating the quality of the association rule-based knowledge plays an important role in the post-processing of the Knowledge Discovery from Databases. More and more interestingness measures are proposed by two approaches (subjective assessment and objective assessment), studying the properties or the attributes of the interestingness measures is important in understanding the nature of the objective interestingness measures. In this paper, we focus primarily on the objective interestingness measures to obtain a general view of recent researches on the nature of the objective interestingness measures, as well as complete a new classification on 109 selected objective interestingness measures on 6 criterions (independence, equilibrium, symmetry, variation, description, and statistics).

Research paper thumbnail of Clustering the objective interestingness measures based on tendency of variation in statistical implications

EAI Endorsed Transactions on Context-aware Systems and Applications, 2016

In recent years, the research cluster of objective interestingness measures has rapidly developed... more In recent years, the research cluster of objective interestingness measures has rapidly developed in order to assist users to choose the appropriate measure for their application. Researchers in this field mainly focus on three main directions: clustering based on the properties of the measures, clustering based on the behavior of measures and clustering tendency of variation in statistical implications. In this paper we propose a new approach to cluster the objective interestingness measures based on tendency of variation in statistical implications. In this proposal, we built the statistical implication data of 31 objective interestingness measures based on the examination of the partial derivatives on four parameters. From this data, two distance matrices of interestingness measures are established based on Euclidean and Manhattan distance. The similarity trees are built based on distance matrix that gave results of 31 measures clustering with two different clustering thresholds.

Research paper thumbnail of A 0.2-μm, 1.8-V, SOI, 550-MHZ, 64-b PowerPC microprocessor with copper interconnects

IEEE Journal of Solid-State Circuits, 1999

A 550-MHz 64-b PowerPC processor in 0.2-um silicon-on-insulator (SOI) copper technology achieves ... more A 550-MHz 64-b PowerPC processor in 0.2-um silicon-on-insulator (SOI) copper technology achieves a 22% frequency gain over a similar design in a CMOS bulk technology. Performance gains are 15%-40% at the circuit level, 24%-28% for critical paths. Unique SOI design aspects such as history effect, lowered noise margins, parasitic bipolar current, and selfheating are considered.

Research paper thumbnail of Statistical Implicative Similarity Measures for User-based Collaborative Filtering Recommender System

International Journal of Advanced Computer Science and Applications, 2016

This paper proposes a new similarity measures for User-based collaborative filtering recommender ... more This paper proposes a new similarity measures for User-based collaborative filtering recommender system. The similarity measures for two users are based on the Implication intensity measures. It is called statistical implicative similarity measures (SIS). This similarity measures is applied to build the experimental framework for User-based collaborative filtering recommender model. The experiments on MovieLense dataset show that the model using our similarity measures has fairly accurate results compared with User-based collaborative filtering model using traditional similarity measures as Pearson correlation, Cosine similarity, and Jaccard.

Research paper thumbnail of Collaborative Filtering Recommendation Based on Statistical Implicative Analysis

Advances in Computational Collective Intelligence, 2020

Collaborative filtering is probably the most familiar and most widely implemented recommendation ... more Collaborative filtering is probably the most familiar and most widely implemented recommendation algorithm. However, traditional collaborative filtering methods focus only on rating data to generate recommendation; they do not consider useful information like item genre and evaluation time, which affect the quality of the system's recommendation seriously. In similarity computation, traditional algorithms use all items; they do not introduce genre component in correlation between user and item. Furthermore, they do not consider the influence of time on user's interests; giving the same treatment to user's score at different time. To address this issue, a new item-based collaborative filtering algorithm is proposed to exploit genre information in each item and reflect dynamic changes over time of user's preferences. The proposed algorithm endows each score with a weight function which keeps user's recent, long and periodic interest, and attenuate user's old short interest. Experimental results from Movielens data set show that the new algorithm outperforms the traditional item-based collaborative filtering algorithms.

Research paper thumbnail of A Lasso-based Collaborative Filtering Recommendation Model

International Journal of Advanced Computer Science and Applications, 2022

Research paper thumbnail of Collaborative Filtering Recommendation Based on Statistical Implicative Analysis

Communications in computer and information science, 2020

Research paper thumbnail of Similarity Kernel for User-based Collaborative Filtering Recommendation System

EAI endorsed transactions on context-aware systems and applications, Jul 6, 2017

Research paper thumbnail of Similarity Kernel for User-based Collaborative Filtering Recommendation System

EAI Endorsed Transactions on Context-aware Systems and Applications, 2017

Research paper thumbnail of Interestingnesslab: A Framework for Developing and Using Objective Interestingness Measures

Advances in Intelligent Systems and Computing, 2016

The objective interestingness measures play an important role in data mining because they are use... more The objective interestingness measures play an important role in data mining because they are used for mining, filtering and ranking the patterns. However, there is no research that collects the measures fully as well as there is no tool that can: automatically calculate the interestingness values of the patterns by using those measures, and is the framework for rapidly developing the applications related to objective interestingness measures. This paper describes Interestingnesslab - a tool of the objective interestingness measures is developed in the R language. The main functions of the tool are: mining a set of association rules and presenting them by the cardinalities (\(n,n_{X},n_{Y},n_{X\overline{Y}}\)), calculating the interestingness value of an association rule according to 1 of 109 collected measures; calculating the interestingness values of the whole rule set in many measures selected by the user; discovering the tendencies in a data set and recommending the top N items to the user; and studying the specific behavior of a set of interestingness measures in the context of a specific dataset and in an exploratory data analysis perspective. With Interestingnesslab, the user can easily and quickly reuse its functions to develop his/her own applications.

Research paper thumbnail of Context-Aware Recommendation with Objective Interestingness Measures

Context-aware recommender systems researches now concentrate on adjusting recommendation results ... more Context-aware recommender systems researches now concentrate on adjusting recommendation results for situations specific context of the users. These studies suggest many ways to integrate user contextual information into the recommendation process such as using topic hierarchies with matrix factorization techniques to improve context-aware recommender systems, measuring frequency-based similarity for context-aware recommender systems, collecting data from social networking to support context-aware recommender systems, and so on. However, these studies mainly focus on the development of context-aware recommendation algorithms to propose items to users in a particular situation and do not care about the extent of contextual involvement in the recommendation process to make recommendation results. In this article, we propose a new approach for context-aware recommender systems based on objective interestingness measures to consider the contextual relationship of the users in the recomm...

Research paper thumbnail of Collaborative Recommenderation Based on Statistical Implication Rules

Journal of Computer Science and Cybernetics, 2018

In recent research, many approaches based on association rules have been proposed to improve the ... more In recent research, many approaches based on association rules have been proposed to improve the accuracy of recommender systems. These approaches are primarily based on Apriori data mining algorithm in order to generate the association rules and apply them to improving the recommendation results. However, these approaches also reveal some disadvantages of the system, such as taking a longer time for generating association rules; applying the Apriori algorithm on rating sparse matrix resulting in irrelevant information and causing poor recommendation results to target users and association rules generated primarily relying on given threshold of Support and Confidence measures leading to the focus on the majority of rules and ignoring the astonishment of rules to affect the recommendation results. In this study, we propose a new model for collaborative filtering recommender systems: The collaborative recommendation is based on statistical implication rules (IIR); Different from colla...

Research paper thumbnail of Context-Similarity Collaborative Filtering Recommendation

Research paper thumbnail of Classification of objective interestingness measures

EAI Endorsed Transactions on Context-aware Systems and Applications, 2016

The creation of the interestingness measures for evaluating the quality of the association rule-b... more The creation of the interestingness measures for evaluating the quality of the association rule-based knowledge plays an important role in the post-processing of the Knowledge Discovery from Databases. More and more interestingness measures are proposed by two approaches (subjective assessment and objective assessment), studying the properties or the attributes of the interestingness measures is important in understanding the nature of the objective interestingness measures. In this paper, we focus primarily on the objective interestingness measures to obtain a general view of recent researches on the nature of the objective interestingness measures, as well as complete a new classification on 109 selected objective interestingness measures on 6 criterions (independence, equilibrium, symmetry, variation, description, and statistics).

Research paper thumbnail of Clustering the objective interestingness measures based on tendency of variation in statistical implications

EAI Endorsed Transactions on Context-aware Systems and Applications, 2016

In recent years, the research cluster of objective interestingness measures has rapidly developed... more In recent years, the research cluster of objective interestingness measures has rapidly developed in order to assist users to choose the appropriate measure for their application. Researchers in this field mainly focus on three main directions: clustering based on the properties of the measures, clustering based on the behavior of measures and clustering tendency of variation in statistical implications. In this paper we propose a new approach to cluster the objective interestingness measures based on tendency of variation in statistical implications. In this proposal, we built the statistical implication data of 31 objective interestingness measures based on the examination of the partial derivatives on four parameters. From this data, two distance matrices of interestingness measures are established based on Euclidean and Manhattan distance. The similarity trees are built based on distance matrix that gave results of 31 measures clustering with two different clustering thresholds.

Research paper thumbnail of A 0.2-μm, 1.8-V, SOI, 550-MHZ, 64-b PowerPC microprocessor with copper interconnects

IEEE Journal of Solid-State Circuits, 1999

A 550-MHz 64-b PowerPC processor in 0.2-um silicon-on-insulator (SOI) copper technology achieves ... more A 550-MHz 64-b PowerPC processor in 0.2-um silicon-on-insulator (SOI) copper technology achieves a 22% frequency gain over a similar design in a CMOS bulk technology. Performance gains are 15%-40% at the circuit level, 24%-28% for critical paths. Unique SOI design aspects such as history effect, lowered noise margins, parasitic bipolar current, and selfheating are considered.

Research paper thumbnail of Statistical Implicative Similarity Measures for User-based Collaborative Filtering Recommender System

International Journal of Advanced Computer Science and Applications, 2016

This paper proposes a new similarity measures for User-based collaborative filtering recommender ... more This paper proposes a new similarity measures for User-based collaborative filtering recommender system. The similarity measures for two users are based on the Implication intensity measures. It is called statistical implicative similarity measures (SIS). This similarity measures is applied to build the experimental framework for User-based collaborative filtering recommender model. The experiments on MovieLense dataset show that the model using our similarity measures has fairly accurate results compared with User-based collaborative filtering model using traditional similarity measures as Pearson correlation, Cosine similarity, and Jaccard.

Research paper thumbnail of Collaborative Filtering Recommendation Based on Statistical Implicative Analysis

Advances in Computational Collective Intelligence, 2020

Collaborative filtering is probably the most familiar and most widely implemented recommendation ... more Collaborative filtering is probably the most familiar and most widely implemented recommendation algorithm. However, traditional collaborative filtering methods focus only on rating data to generate recommendation; they do not consider useful information like item genre and evaluation time, which affect the quality of the system's recommendation seriously. In similarity computation, traditional algorithms use all items; they do not introduce genre component in correlation between user and item. Furthermore, they do not consider the influence of time on user's interests; giving the same treatment to user's score at different time. To address this issue, a new item-based collaborative filtering algorithm is proposed to exploit genre information in each item and reflect dynamic changes over time of user's preferences. The proposed algorithm endows each score with a weight function which keeps user's recent, long and periodic interest, and attenuate user's old short interest. Experimental results from Movielens data set show that the new algorithm outperforms the traditional item-based collaborative filtering algorithms.