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In the modern world, people face an explosion of information and difficulty to find the right choice of their interest. Nowadays, people show interest in online shopping to meet their demands increasingly. For researchers and students, finding and buying the desired books from online shops is very tedious work. Recently Recommender System is an excellent tool to deal with such problems, but the Recommender System is suffering from multiple problems such as data sparsity, cold-start, and inaccuracy. To address these problems, we propose Deep Edu a novel Deep Neural Collaborative Filtering for educational services recommendation. A Deep Edu architecture consists of three parts of a Deep Neural Network model (such as input layer, a multilayered perceptron, and an output layer). The Deep Edu provides the following contributions: first, the users' identifier and books identifier features are mapped into N-dimensional dense embedding vectors, second, the Multi-Layer-Perceptron (MLP) takes the N-dimensional and non-linear features. To increase the performance of Deep Edu in all metrics, we proposed the advance Loss function. Equipped with the following, Deep Edu not only capable of learning the N-dimensional and non-linear interactions between users' identifier and books identifier, but moreover, it also considerably mitigates the cold-start, data sparsity, and inaccuracy problem. Over significant experiments performed on real-world good books dataset, the results show that Deep Edu's recommendation performance obviously outperforms existing Educational services recommendation methods.

Owing to the ever-growing popularity of mobile computing, a large number of services have been developed for a variety of users. Considering this, recommending useful services to users is an urgent problem that needs to be addressed. Collaborative filtering (CF) approaches have been successfully adopted for services recommendation. Nevertheless, the prediction accuracy of the existing CF approaches is likely to reduce due to many reasons, such as inability to use side information and high data sparsity, which further lead to low quality of services recommendation. In order to solve these problems, some model-based CF approaches have been proposed. In this paper, we propose a novel quality of service prediction approach based on probabilistic matrix factorization (PMF), which has the capability of incorporating network location (an important factor in mobile computing) and implicit associations among users and services. First, we propose a novel clustering method that is capable of utilizing network location to cluster users. Based on the clustering results, we further propose an enhanced PMF model. The proposed model also incorporates the implicit associations among users and services. In addition, our model incorporates the implicit relationships between the users and the services. We conducted experiments on one real-world data set, and the experimental results show that our model outperforms the compared methods.

The numbers of web services are growing rapidly in recent years. One of the most challenging issues in service computing is the personalized recommendation of Web services. Most of the current research recommends services based on Quality of Service (QoS)-aware data with few considerations of service-side factors, such as service functions. In this paper, a new QoS-aware Web service recommendation model based on user and service clustering (RMUSC) is proposed to gain an advance in recommended accuracy. Firstly, similar users are clustered together by a Top-N similarity algorithm through the user QoS records. Secondly, a K-means++ based filtering service cluster is established. Finally, a user and services collaborative scheme is exploited and obtains potential user QoS preferences to generate recommendations. The experimental results show that when the density of the service invocation matrix is 5%, 10% and 20%. the average absolute error (MAE) and root mean square error (RMSE) of RMUSC are lower than those of other methods. [ABSTRACT FROM AUTHOR]