Authors:
Hiep Luong
1
;
Tin Huynh
2
;
Susan Gauch
1
and
Kiem Hoang
2
Affiliations:
1
University of Arkansas, United States
;
2
University of Information Technology, Vietnam
Keyword(s):
Recommender Systems, Social Network Analysis, Publication History, kNN, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Computational Intelligence
;
Evolutionary Computing
;
Interactive and Online Data Mining
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
;
User Profiling and Recommender Systems
Abstract:
The impact of a publication venue is a major consideration for researchers and scholars when they are deciding where to publish their research results. By selecting the right conference or journal to which to submit a new paper minimizes the risk of wasting the long review time for a paper that is ultimately rejected. This task also helps to recommend appropriate conference venues of which authors may not be aware or to which colleagues often submit their papers. Traditional ways of scientific publication recommendation using content-based analysis have shown drawbacks due to mismatches caused by ambiguity in text comparisons and there is also much more to selecting an appropriate venue than just topical-matching. In our work, we are taking advantage of actual and interactive relationships within the academic community, as indicated by co-authorship, paper review or event co-organizing activities, to support the venue recommendation process. Specifically, we present a new social netw
ork-based approach that automatically finds appropriate publication venues for authors’ research paper by exploring their network of related co-authors and other researchers in the same field. We also recommend appropriate publication venues to a specific user based on her relation with the program committee research activities and with others in her network who have similar paper submission preferences. This paper also presents more accurate and promising results of our social network-based in comparison with the baseline content-based approach. Our experiment, which was empirically tested over a large set of scientific papers published in 16 different ACM conferences, showed that analysing an academic social network would be useful for a variety of recommendation tasks including trend of publications, expert findings, and research collaborations, etc.
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