Research Direction

• Smart service systems: smart services in commerce, marketing, education; personalization and context-aware services and systems. Applying journey visualization, clustering, prediction, and decision support techniques to the above problems. In particular, focusing on the smart service system framework, specific smart service system building problems in analyzing customer journeys, predicting customer churn rates, etc.
• Building and representing knowledge (knowledge representation) and semantic web (semantic web): Techniques (NLP, NER) build knowledge models from extracting unstructured data.
• Text mining and information retrieval: Deep learning for text search techniques: text classification, text search, information extraction, question and answer.
• Recommender system: research recommendation algorithms and apply them to build recommendation systems for specific fields.
• Advanced Database: Research and apply new types of data storage (graph store, time-series data, ...) and knowledge graph (knowledge graph) in the field of data analysis and mining in applications. information system application field.
• Big data mining: Refers to the process of extracting meaningful patterns, insights, and knowledge from large and complex datasets. It involves applying data mining techniques to vast amounts of structured, semi-structured, and unstructured data to discover hidden patterns, correlations, trends, and other valuable information.
• Business intelligence (BI) support systems: Refer to technologies, applications, and practices used to collect, integrate, analyze, and present business information. The primary goal of BI systems is to support better decision-making within organizations by providing insights derived from data.

Research Projects

Scientific Publication

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