2 research outputs found
Scientific Article Recommendation: Exploiting Common Author Relations and Historical Preferences
Scientific article recommender systems are playing an increasingly important
role for researchers in retrieving scientific articles of interest in the
coming era of big scholarly data. Most existing studies have designed unified
methods for all target researchers and hence the same algorithms are run to
generate recommendations for all researchers no matter which situations they
are in. However, different researchers may have their own features and there
might be corresponding methods for them resulting in better recommendations. In
this paper, we propose a novel recommendation method which incorporates
information on common author relations between articles (i.e., two articles
with the same author(s)). The rationale underlying our method is that
researchers often search articles published by the same author(s). Since not
all researchers have such author-based search patterns, we present two
features, which are defined based on information about pairwise articles with
common author relations and frequently appeared authors, to determine target
researchers for recommendation. Extensive experiments we performed on a
real-world dataset demonstrate that the defined features are effective to
determine relevant target researchers and the proposed method generates more
accurate recommendations for relevant researchers when compared to a Baseline
method.Comment: 13 pages, 14 figure
Scientific Paper Recommendation: A Survey
Globally, recommendation services have become important due to the fact that
they support e-commerce applications and different research communities.
Recommender systems have a large number of applications in many fields
including economic, education, and scientific research. Different empirical
studies have shown that recommender systems are more effective and reliable
than keyword-based search engines for extracting useful knowledge from massive
amounts of data. The problem of recommending similar scientific articles in
scientific community is called scientific paper recommendation. Scientific
paper recommendation aims to recommend new articles or classical articles that
match researchers' interests. It has become an attractive area of study since
the number of scholarly papers increases exponentially. In this survey, we
first introduce the importance and advantages of paper recommender systems.
Second, we review the recommendation algorithms and methods, such as
Content-Based methods, Collaborative Filtering methods, Graph-Based methods and
Hybrid methods. Then, we introduce the evaluation methods of different
recommender systems. Finally, we summarize open issues in the paper recommender
systems, including cold start, sparsity, scalability, privacy, serendipity and
unified scholarly data standards. The purpose of this survey is to provide
comprehensive reviews on scholarly paper recommendation