We investigate a novel perspective to the development of effective algorithms for contact recommendation in social networks, where the problem consists of automatically predicting people that a given user may wish or benefit from connecting to in the network. Specifically, we explore the connection between contact recommendation and the text information retrieval (IR) task, by investigating the adaptation of IR models (classical and supervised) for recommending people in social networks, using only the structure of these networks.

We first explore the use of adapted unsupervised IR models as direct standalone recommender systems. Seeking additional effectiveness enhancements, we further explore the use of IR models as neighbor selection methods, in place of common similarity measures, in user-based and item-based nearest-neighbors (kNN) collaborative filtering approaches. On top of this, we investigate the application of learning to rank approaches borrowed from text IR to achieve additional improvements.

We report thorough experiments over data obtained from Twitter and Facebook where we observe that IR models, particularly BM25, are competitive compared to state-of-the art contact recommendation methods. We provide further empirical analysis of the additional effectiveness that can be achieved by the integration of IR models into kNN and learning to rank schemes. Our research shows that the IR models are effective in three roles: as direct contact recommenders, as neighbor selectors in collaborative filtering and as samplers and features in learning to rank.

我们调查了社交网络中用于联系推荐的有效算法的发展的新颖观点，其中问题包括自动预测给定用户可能希望从网络中连接到的人或从中受益。具体来说，我们通过研究IR模型（经典和受监督）在社交网络中推荐人的适应性，仅使用这些模型的结构，就探索了联系推荐和文本信息检索（IR）任务之间的联系。

我们首先探索将自适应无监督IR模型用作直接独立推荐系统。为了提高效率，我们进一步探索了在基于用户和基于项目的最近邻居（kNN）协同过滤方法中，将IR模型用作邻居选择方法，以代替常见的相似性度量。在此之上，我们研究了学习对排名从文本IR借用的方法进行排名以实现其他改进的应用。

我们对从Twitter和Facebook获得的数据进行了全面的实验，结果发现IR模型（尤其是BM25）与最新的联系人推荐方法相比具有竞争力。我们提供了将IR模型集成到kNN和学习排名方案中可以实现的其他有效性的进一步实证分析。我们的研究表明，IR模型在以下三个方面很有效：作为直接联系推荐者，作为协作筛选的邻居选择器以及作为学习排名的采样器和功能。