Discovering diverse human behavior from two-dimensional preferences
| Autor: | Yung-Ting Chuang, Huan Lin Chu, Pu Tai Yang, Cheng Kui Huang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Information management
Information Systems and Management business.industry Computer science 02 engineering and technology Human behavior computer.software_genre Management Information Systems Ranking Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Software Natural language processing |
| Zdroj: | Knowledge-Based Systems. 152:11-25 |
| ISSN: | 0950-7051 |
| DOI: | 10.1016/j.knosys.2018.04.003 |
| Popis: | Among the many types of ambiguous and diverse human behaviors, preference ranking and preferred temporal relationships are two human subjective perceptions, and both can be expressed as sequences. For example, information management researchers prefer journal A over B, and this preference can be represented as the ranking sequence: (A > B). When the order of submission is first A and then B, it can be expressed as the temporal sequence: (A → B). In practice, these two preference sequences may be applicable to people with regard to the same items at the same time, which is called “two-dimensional preference” in this study. Based on these concepts, this study defines a novel model and an associated algorithm for mining two-dimensional patterns by combining preference ranking and temporal sequences. The discovered two-dimensional patterns can be categorized into eight types, including consensus, ranking-compromise, temporal-compromise, and conflict patterns. Two experiments in two application areas, namely journal submission and stock purchase, were designed to collect authentic datasets, and demonstrate their managerial meaning. The experimental results show that in most cases, the ordering of the temporal sequences follows the preference ranking sequence, except for several two-dimensional patterns involving high-risk items. |
| Databáze: | OpenAIRE |
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