Structural Diversity in Social Recommender Systems
نویسندگان
چکیده
Online social networks have become important for sharing, discovery, communication, and networking. Recommender systems are an essential part of any social network. For example, recommending people to connect with is essential for the growth of the network since an online social network is only partially observed and two people might know each other but may not be connected. In this paper, we analyze data from LinkedIn, the largest online professional social network, which recommends other members to connect through its “People You May Know” feature. Analyzing the effect of structural diversity on the invitation rate from such member recommendations, we find that higher connection density and lower structural diversity results in a higher connection invitation rate. We also analyze and study the effects of structural diversity of members’ connection networks on their engagement on the LinkedIn network. General Terms: Social Recommender Systems, Structural Diversity, Engagement
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