Due to the change in attitudes and lifestyles, people expect to find new partners and friends via various ways now-a-days. Online dating networks create a network for people to meet each other and allow making contact with different objectives of developing a personal, romantic or sexual relationship. Due to the higher expectation of users, online matching companies are trying to adopt recommender systems. However, the existing recommendation techniques such as content-based, collaborative filtering or hybrid techniques focus on users explicit contact behaviors but ignore the implicit relationship among users in the network. This paper proposes a social matching system that uses past relations and user similarities in finding potential matches. The proposed system is evaluated on the dataset collected from an online dating network. Empirical analysis shows that the recommendation success rate has increased to 31% as compared to the baseline success rate of 19%.ud
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机译：由于态度和生活方式的变化，人们希望现在通过各种方式找到新的伙伴和朋友。在线约会网络创建了一个人与人交流的网络，并允许他们与建立个人，浪漫或性关系的不同目标进行联系。由于用户的期望较高，在线匹配公司正在尝试采用推荐系统。但是，现有的推荐技术（例如基于内容的协作过滤或混合技术）专注于用户的显式联系行为，却忽略了网络中用户之间的隐式关系。本文提出了一种社交匹配系统，该系统使用过去的关系和用户相似性来寻找潜在的匹配项。对从在线约会网络收集的数据集评估提出的系统。实证分析表明，与基准成功率19％相比，推荐成功率已提高到31％。 ud
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