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Explainable Recommendation for Event Sequences: A Visual Analytics Approach
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Explainable Recommendation for Event Sequences: A Visual Analytics Approach. |
| 개인저자 | Du, Fan. |
| 단체저자명 | University of Maryland, College Park. Computer Science. |
| 발행사항 | [S.l.]: University of Maryland, College Park., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 220 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-11B(E). Dissertation Abstract International |
| ISBN | 9780438149342 |
| 학위논문주기 | Thesis (Ph.D.)--University of Maryland, College Park, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Advisers: Ben Shneiderman |
| 요약 | People use recommender systems to improve their decisions, for example, item recommender systems help them find films to watch or books to buy. Despite the ubiquity of item recommender systems, they can be improved by giving users greater transp |
| 요약 | This dissertation's main contribution is the use of both record attributes and temporal event information as features to identify similar records and provide appropriate recommendations. While traditional item recommendations are generated based |
| 요약 | This dissertation applies a visual analytics approach to present and explain recommendations of event sequences. It presents a workflow for event sequence recommendation that is implemented in EventAction. Results from empirical studies show tha |
| 요약 | This dissertation contributes an analytical workflow, an interactive system, and design guidelines identified in empirical studies and case studies, opening new avenues of research in explainable event sequence recommendations based on personal |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
