On Deep Learning for Trust-Aware Recommendations in Social Networks
On Deep Learning for Trust-Aware Recommendations in Social Networks
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DOI：
10.1109/tnnls.2016.2514368
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发表时间：
2017-05-01
影响因子：
10.4
通讯作者：
Wu, Zhaohui
中科院分区：
文献类型：
--
作者：
Deng, Shuiguang;Huang, Longtao;Wu, Zhaohui
随着在线社交网络的出现，基于社交网络的推荐方式得到了广泛的应用。这种方法的主要好处是能够处理冷启动用户的问题。除了社交网络，用户信任信息在获取可靠推荐方面也发挥着重要作用。虽然矩阵分解在推荐系统中占据主导地位，但推荐在很大程度上依赖于用户和项目潜在特征向量的初始化。针对这些挑战，我们提出了一种新的基于信任的社交网络推荐方法。特别是，我们试图利用深度学习来确定MF中信任感知社交推荐的初始化，并区分用户信任友谊中的社区效应。提出了一种两阶段推荐过程，在初始阶段利用深度学习，综合用户兴趣和信任朋友的兴趣，以及社区效应对推荐的影响。我们在真实的社会网络数据上进行了大量的实验，与其他最先进的方法相比，我们的方法证明了我们所提出的方法的准确性和有效性。
With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommender systems, the recommendation largely relies on the initialization of the user and item latent feature vectors. Aiming at addressing these challenges, we develop a novel trust-based approach for recommendation in social networks. In particular, we attempt to leverage deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user's trusted friendships. A two-phase recommendation process is proposed to utilize deep learning in initialization and to synthesize the users' interests and their trusted friends' interests together with the impact of community effect for recommendations. We perform extensive experiments on real-world social network data to demonstrate the accuracy and effectiveness of our proposed approach in comparison with other state-of-the-art methods.