With the great development of mobile services, the Quality of Services (QoS) becomes an essential factor to meet end users’ personalized requirement on the nonfunctional performance of mobile services. However, most of the QoS values in real cases are unattainable because a service user would only invoke some specific mobile services. Therefore, how to predict the missing QoS values and recommend high-quality services to end users becomes a significant challenge in mobile service recommendation research. Previous QoS prediction researches demonstrate that the nonfunctional performance of mobile services is closely related to users’ location information. However, most location-aware QoS prediction methods ignore the premise that the obtainable QoS values observed by different users in same location region would probably be untrustworthy, which will lead to inaccurate and unreliable prediction results. To make credible location-aware QoS prediction, we propose a hybrid matrix factorization method integrated location and reputation information (LRMF) to predict the unattainable QoS values. Our approach firstly cluster users into different locational region based on their geographical distribution, and then we compute users’ reputation to identify untrustworthy users in every locational region. Finally, the unknown QoS values can be predicted by integrating locational cluster information and users’ reputation into a hybrid matrix factorization model. Comprehensive experiments are conducted on a public QoS dataset which contains sufficient real-world service invocation records. The evaluation results indicate that our LRMF method can effectively reduce the impact of unreliable users on QoS prediction and make credible mobile service recommendation.

In this study, we focus on the problem of information expiration when using the traditional collaborative filtering algorithm and propose a new collaborative filtering algorithm by integrating the time factor (ITWCF). This algorithm considers information influence attenuation over time, introduces an information retention period based on the information half‐value period, and proposes a time‐weighted function, which is applied to the nearest neighbor selection and score prediction to assign different time weights to the scores. In addition, to further improve the quality of the nearest neighbor selection and alleviate the problem of data sparsity, a method of calculating users’ sentiment tendency by analysis of user review features is proposed to mine users’ attitudes about the reviewed items, which expands the score matrix. The time factor and sentiment tendency are then integrated into the K‐means clustering algorithm to select the nearest neighbor. A hybrid collaborative filtering model (TWCHR) based on the improved K‐means clustering algorithm is then proposed, by combining item‐based and user‐based collaborative filtering. Finally, the experimental results show that the proposed algorithm can address the time effect and sentiment analysis in recommendations and improve the predictive performance of the model.

In the collaborative filtering (CF) recommendation applications, the sparsity of user rating data, the effectiveness of cold start, the strategy of item information neglection, and user profiles construction are critical to both the efficiency and effectiveness of the recommendation algorithm. In order to solve the above problems, a personalized recommendation approach combining semisupervised support vector machine and active learning (AL) is proposed in this paper, which combines the benefits of both TSVM (Transductive Support Vector Machine) and AL. Firstly, a “maximum-minimum segmentation” of version space-based AL strategy is developed to choose the most informative unlabeled samples for human annotation; it aims to choose the least data which is enough to train a high-quality model. And then, an AL-based semisupervised TSVM algorithm is proposed to make full use of the distribution characteristics of unlabeled samples by adding a manifold regularization into objective function, which is helpful to make the proposed algorithm to overcome the traditional drawbacks of TSVM. Furthermore, during the procedure of recommendation model construction, not only user behavior information and item information, but also demographic information is utilized. Due to the benefits of the above design, the quality of unlabeled sample annotation can be improved; meanwhile, both the data sparsity and cold start problems are alleviated. Finally, the effectiveness of the proposed algorithm is verified based on UCI datasets, and then it is applied to personalized recommendation. The experimental results show the superiority of the proposed method in both effectiveness and efficiency.