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Improving Geospatial Data Search Ran...

Jiang, Yongyao.

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Improving Geospatial Data Search Ranking Using Deep Learning and User Behaviour Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Improving Geospatial Data Search Ranking Using Deep Learning and User Behaviour Data./
作者:	Jiang, Yongyao.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	107 p.
附註:	Source: Dissertation Abstracts International, Volume: 80-04(E), Section: A.
Contained By:	Dissertation Abstracts International80-04A(E).
標題:	Information science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10933269
ISBN:	9780438738836

Improving Geospatial Data Search Ranking Using Deep Learning and User Behaviour Data.
Jiang, Yongyao.

Improving Geospatial Data Search Ranking Using Deep Learning and User Behaviour Data. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 107 p.

Source: Dissertation Abstracts International, Volume: 80-04(E), Section: A.

Thesis (Ph.D.)--George Mason University, 2018.

Finding Earth science data has been a challenging problem given both the quantity of data available and the heterogeneity of the data across a wide variety of domains. Current search engines in most geospatial data portals tend to induce end users to focus on one single data attribute. This approach largely fails to take account of users' multiple and dynamic preferences for geospatial data, and hence may likely result in a less than optimal user experience in discovering the most applicable dataset out of a vast range of available datasets. With users interacting with search engines, sufficient information is already hidden in the log files. Compared with explicit feedback data, information that can be derived/extracted from log files is virtually free and substantially more timely. In this dissertation, I propose a deep learning based ranking framework that can learng and update the ranking function based on user behavior data. The contributions of this framework include 1) a log processor that that can ingest, extract user access pattern and create training data from Web log in a batch mode/real-time; 2) a query understanding module to better interpret users' search intent using web log processing results and metadata; 3) a feature extractor that identifies ranking features representing users' search interests of geospatial data; and 4) a deep learning based ranking algorithm that automatically learns and updates a ranking function based on user behavior. The search ranking results will be evaluated using precision at K and normalized discounted cumulative gain (NDCG). This research will strengthen ties between Earth observations and user communities by addressing the ranking challenge, the fundamental obstacle in geospatial data discovery. As a proof of concept, I focus on a well-defined domain -- Oceanographic Science, and using NASA JPL's PO.DAAC as an example.

ISBN: 9780438738836Subjects--Topical Terms:

554358
Information science.

Improving Geospatial Data Search Ranking Using Deep Learning and User Behaviour Data.
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