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Gang-Li Liu

dblp:162/1624 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2017
0000-0003-3921-0446ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval
personalized search
0.312017
Personal Web Revisitation by Context and Content Keywords with Relevance Feedback · IEEE Trans. Knowl. Data Eng. 2017
Information retrieval
relevance feedback
0.312017
Personal Web Revisitation by Context and Content Keywords with Relevance Feedback · IEEE Trans. Knowl. Data Eng. 2017
Information retrieval
context-based retrieval
0.112017
Personal Web Revisitation by Context and Content Keywords with Relevance Feedback · IEEE Trans. Knowl. Data Eng. 2017

Methods — techniques the papers use, named apart from their topics

relevance feedback mechanism · 0.3context and content keyword matching · 0.3
YearPublicationVenuePosition
2017 A PDF document re-finding system with a Q&A wizard interface
Gang-Li Liu, Baihui Jiang
Knowl. Based Syst.1
2017 Personal Web Revisitation by Context and Content Keywords with Relevance Feedback
abstract
Getting back to previously viewed web pages is a common yet uneasy task for users due to the large volume of personally accessed information on the web. This paper leverages human's natural recall process of using episodic and semantic memory cues to facilitate recall, and presents a personal web revisitation technique called WebPagePrev through context and content keywords. Underlying techniques for context and content memories' acquisition, storage, decay, and utilization for page re-finding are discussed. A relevance feedback mechanism is also involved to tailor to individual's memory strength and revisitation habits. Our 6-month user study shows that: (1) Compared with the existing web revisitation tool Memento, History List Searching method, and Search Engine method, the proposed WebPagePrev delivers the best re-finding quality in finding rate (92.10 percent), average F1-measure (0.4318), and average rank error (0.3145). (2) Our dynamic management of context and content memories including decay and reinforcement strategy can mimic users' retrieval and recall mechanism. With relevance feedback, the finding rate of WebPagePrev increases by 9.82 percent, average F1-measure increases by 47.09 percent, and average rank error decreases by 19.44 percent compared to stable memory management strategy. Among time, location, and activity context factors in WebPagePrev, activity is the best recall cue, and context+content based re-finding delivers the best performance, compared to context based re-finding and content based re-finding.
Gang-Li Liu, Chaokun Wang
IEEE Trans. Knowl. Data Eng.3
2015 PhotoPrev: Unifying Context and Content Cues to Enhance Personal Photo Revisitation
Gang-Li Liu, Liang Zhao 0023
J. Comput. Sci. Technol.2