SibRank：基于邻居的协作排序的符号二分网络分析
SibRank: Signed Bipartite Network Analysis for Neighbor-based Collaborative Ranking
摘要：
协同排名是一个新兴的领域，推荐系统，利用用户的偏好数据，而不是评级值。不幸的是，基于邻居的协作排名得到了很少的关注，尽管它更灵活和可扩展性。本文提出了一种新的框架，称为SibRank，旨在改善最先进的基于邻居的协作排名方法。SibRank将用户的偏好表示为一个签名二分网络，并通过一种新的个性化排序算法在签名网络中发现相似用户。
英文摘要：
Collaborative ranking is an emerging field of recommender systems that utilizes users' preference data rather than rating values. Unfortunately, neighbor-based collaborative ranking has gained little attention despite its more flexibility and justifiability. This paper proposes a novel framework, called SibRank that seeks to improve the state of the art neighbor-based collaborative ranking methods. SibRank represents users' preferences as a signed bipartite network, and finds similar users, through a novel personalized ranking algorithm in signed networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
