Diversity Preference-Aware Link Recommendation for Online Social
Networks
- URL: http://arxiv.org/abs/2205.10689v1
- Date: Sat, 21 May 2022 22:59:00 GMT
- Title: Diversity Preference-Aware Link Recommendation for Online Social
Networks
- Authors: Kexin Yin, Xiao Fang, Bintong Chen and Olivia Sheng
- Abstract summary: Existing link recommendation methods tend to recommend similar friends to a user but overlook the user's diversity preference.
We propose a new link recommendation problem: the diversity preference-aware link recommendation problem.
We then analyze key properties of the new link recommendation problem and develop a novel link recommendation method to solve the problem.
- Score: 0.2580765958706854
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Link recommendation, which recommends links to connect unlinked online social
network users, is a fundamental social network analytics problem with ample
business implications. Existing link recommendation methods tend to recommend
similar friends to a user but overlook the user's diversity preference,
although social psychology theories suggest the criticality of diversity
preference to link recommendation performance. In recommender systems, a field
related to link recommendation, a number of diversification methods have been
proposed to improve the diversity of recommended items. Nevertheless, diversity
preference is distinct from diversity studied by diversification methods. To
address these research gaps, we define and operationalize the concept of
diversity preference for link recommendation and propose a new link
recommendation problem: the diversity preference-aware link recommendation
problem. We then analyze key properties of the new link recommendation problem
and develop a novel link recommendation method to solve the problem. Using two
large-scale online social network data sets, we conduct extensive empirical
evaluations to demonstrate the superior performance of our method over
representative diversification methods adapted for link recommendation as well
as state-of-the-art link recommendation methods.
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