Diversity Preference-Aware Link Recommendation for Online Social Networks
针对现有链接推荐忽略用户多样性偏好的问题，定义了多样性偏好概念并提出新推荐方法，为不同用户推荐符合其偏好多样性的好友。
Link recommendation, such as “People You May Know” on LinkedIn, recommends links to connect unlinked online social network users. Existing link recommendation methods tend to recommend similar friends to a user but overlook the fact that different users have different diversity preferences when making friends in a social network. That is, some users prefer to connect with friends of similar profiles while some others prefer to befriend those of different profiles. For example, Jane prefers to connect with those primarily majoring in mathematics, whereas Jack prefers to befriend those in many different majors. To address this research gap, we define and operationalize the concept of diversity preference and propose a new link recommendation problem: the diversity preference-aware link recommendation problem. We then develop a novel link recommendation method that recommends friends to cater each user’s diversity preference. Our study informs researchers and practitioners about a new perspective on link recommendation – diversity preference-aware link recommendation. Our study also suggests that recommender systems need to be designed to meet each user’s diversity preference rather than indiscriminately increase the diversity of recommended items for every user.