Welcome to the InfoSci Platform
Reference Hub3
ROVETS: Search Based Socially-Aware Recommendation of Smart Conference Sessions

ROVETS: Search Based Socially-Aware Recommendation of Smart Conference Sessions

Nana Yaw Asabere, Amevi Acakpovi
Copyright: © 2019 |Volume: 11 |Issue: 3 |Pages: 17
ISSN: 1941-6296|EISSN: 1941-630X|EISBN13: 9781522565420|DOI: 10.4018/IJDSST.2019070103
Cite Article Cite Article

MLA

Asabere, Nana Yaw, and Amevi Acakpovi. "ROVETS: Search Based Socially-Aware Recommendation of Smart Conference Sessions." IJDSST vol.11, no.3 2019: pp.30-46. http://doi.org/10.4018/IJDSST.2019070103

APA

Asabere, N. Y. & Acakpovi, A. (2019). ROVETS: Search Based Socially-Aware Recommendation of Smart Conference Sessions. International Journal of Decision Support System Technology (IJDSST), 11(3), 30-46. http://doi.org/10.4018/IJDSST.2019070103

Chicago

Asabere, Nana Yaw, and Amevi Acakpovi. "ROVETS: Search Based Socially-Aware Recommendation of Smart Conference Sessions," International Journal of Decision Support System Technology (IJDSST) 11, no.3: 30-46. http://doi.org/10.4018/IJDSST.2019070103

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

As a result of the tremendous proliferation of sessions at academic conferences, recommending appropriate venues for researchers has become a considerable problem. In this article, the authors propose an innovative recommender algorithm called Recommendation of Venues and Environments Through Social-Awareness (ROVETS). ROVETS seeks to enhance the social awareness of attendees at a smart conference. ROVETS initially employs closeness centrality and Breadth First Search (BFS) to detect potential presenters for a target attendee. Then, the accurate tie strength between the attendees and presenters as well as the degree centrality of the presenters are computed based on similarity of their research interests. Using the computations above, ROVETS generates effective recommendations pertaining to venues for attendees who have high tie strength with presenters. Through a relevant real-world dataset, this article evaluates the proposed recommender algorithm. These experimental results validate that ROVETS exhibits favorable enhancements over other existing state-of-the-art methods.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.