Concentrating on the data sparsity problem and the new user cold-start problem faced by traditional collaborative filtering algorithm in mobile recommender system,an approach named CSMSR (Context-similarity and Social-network based Mobile Service Recommendation) is proposed.The approach integrates mobile users'' context information and social network information into collaborative filtering recommendation process.Firstly,it imports the user-based context similarities into the personalized service recommendation process.Secondly,it searches the corresponding nearest neighbors for each mobile user according to the given mobile users'' ratings and the mining social network.Finally,it predicts unknown mobile users'' preferences and generates recommendations.The experimental results show that CSMSR outperforms the existing service recommendation methods,such as TNCF,SRMTC and CF-DNC in terms of MAE (Mean Absolute Error) and P@N,and it performs well in finding out the interested services of users and enhancing the user experience.%针对传统的基于协同过滤的移动服务推荐方法存在的数据稀疏性和用户冷启动问题,提出一种基于上下文相似度和社会网络的移动服务推荐方法(Context-similarity and Social-network based Mobile Service Recommendation,CSMSR).该方法将基于用户的上下文相似度引入个性化服务推荐过程,并挖掘由移动用户虚拟交互构成的社会关系网络,按照信任度选取信任用户;然后结合基于用户评分相似度计算发现的近邻,分别从相似用户和信任用户中选择相应的邻居用户,对目标用户进行偏好预测和推荐.实验表明,与已有的服务推荐方法TNCF、SRMTC及CF-DNC相比,CSMSR方法有效地缓解数据稀疏性并提高推荐准确率,有利于发现用户感兴趣的服务,提升用户个性化服务体验.
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