上下文感知的移动社交网络推荐算法研究

摘要

Although patterns of human activity show a large degree of freedom, they exhibit structural patterns subjected by geographic and social constraints. Aiming at various problems of personalized recommendation in mobile networks, a social network recommendation algorithm is proposed with a variety of context-aware information and combined with a series of social network analysis methods. Based on geographical location and temporal information, potential social relations among users are mined deeply to find the most similar set of users for the target user, then recommendations are carried out incorporating with social relations of the mobile users to effectively solve the problem of recommendation precision. The above study can not only help LBSN designers and developers to better understand their users and grasp their want, but also help to refine the design of their system to provide users with more appropriate applications and services. The experimental results on the real-world dataset verify the feasibility and effectiveness of the proposed algorithm, and it has higher prediction accuracy compared with existing recommendation algorithms.%尽管人类活动模式表现出较大的自由度，但也表现出受制于地理和社会限制的结构化模式。针对移动通信网络领域中个性化服务推荐问题，结合社会化网络分析方法，提出一种融合多种上下文信息的社交网络推荐算法。该算法在利用用户的地理位置和时间信息的基础上，深入挖掘潜在的用户社会关系，辅助用户寻找与其偏好相似的用户，然后结合移动用户的社会关系进行相应的推荐，有效解决推荐的准确性问题。这些发现有助于LBSN类系统设计和开发人员更好地了解用户，获知用户的需求，最终完善自己的设计，为用户提供更好的应用服务。在真实数据集上的实验结果验证该算法的可行性和有效性，并且与现有推荐算法相比，具有更高的预测准确度。