With the rapid development of information technology, information is generated at an unprecedented rate. Users are in great need of recommender systems to provide the potential friends or interested items for them. Social (i.e. friend) recommendation and behavior (i.e. item) recommendation are two types of popular services in real-world applications. Although researchers have proposed various models for each task, a unified model to address both tasks elegantly and effectively is still in demand. In this paper, we propose a model called SBRNE which integrates social and behavior recommendations into a unified framework through modeling social and behavior information simultaneously. Specifically, SBRNE models social and behavior information simultaneously via employing users’ latent interests as a bridge, and derives improved performance on both social and behavior recommendation tasks. In addition, by introducing an efficient network embedding procedure, users’ latent representations are advanced, and effectiveness and efficiency of recommendation tasks are improved accordingly. Results on both real-world and synthetic datasets demonstrate that: 1). SBRNE outperforms selected baselines on social and behavior recommendation tasks; 2). SBRNE performs stable on recommendation tasks for cold-start users; 3). The network embedding procedure can improve the effectiveness of SBRNE; 4). The hyper-parameter learning procedure can improve both the effectiveness and efficiency of SBRNE.

随着信息技术的飞速发展，信息以前所未有的速度生成。用户非常需要推荐系统为他们提供潜在的朋友或感兴趣的物品。社会（即朋友）推荐和行为（即项目）推荐是现实应用中的两种流行服务。尽管研究人员为每个任务提出了各种模型，但是仍然需要一个统一的模型来优雅有效地解决这两个任务。在本文中，我们提出了一个名为SBRNE的模型，该模型通过同时建模社交和行为信息将社交和行为建议整合到一个统一的框架中。具体来说，SBRNE通过利用用户的潜在兴趣作为桥梁，同时对社交和行为信息进行建模，并提高了社交和行为推荐任务的绩效。另外，通过引入有效的网络嵌入过程，提高了用户的潜在表示，并且相应地提高了推荐任务的有效性和效率。真实数据集和综合数据集上的结果均表明：1）。SBRNE在社交和行为推荐任务方面的表现优于选定的基准；2）。SBRNE为冷启动用户执行稳定的推荐任务；3）。网络嵌入程序可以提高SBRNE的有效性。4）。超参数学习过程可以提高SBRNE的有效性和效率。从而提高了推荐任务的有效性和效率。真实数据集和综合数据集上的结果均表明：1）。SBRNE在社交和行为推荐任务方面的表现优于选定的基准；2）。SBRNE为冷启动用户执行稳定的推荐任务；3）。网络嵌入程序可以提高SBRNE的有效性。4）。超参数学习过程可以提高SBRNE的有效性和效率。从而提高了推荐任务的有效性和效率。真实数据集和综合数据集上的结果均表明：1）。SBRNE在社交和行为推荐任务方面的表现优于选定的基准；2）。SBRNE为冷启动用户执行稳定的推荐任务；3）。网络嵌入程序可以提高SBRNE的有效性。4）。超参数学习过程可以提高SBRNE的有效性和效率。