A hybrid collaborative filtering algorithm based on the user preferences and item features is proposed. A thorough investigation of Collaborative Filtering (CF) techniques preceded the development of this algorithm. The proposed algorithm improved the user-item similarity approach by extracting the item feature and applying various item features' weight to the item to confirm different item features. User preferences for different item features were obtained by employing user evaluations of the items. It is expected that providing better recommendations according to preferences and features would improve the accuracy and efficiency of recommendations and also make it easier to deal with the data sparsity. In addition, it is expected that the potential semantics of the user evaluation model would be revealed. This would explain the recommendation results and increase accuracy. A portion of the MovieLens database was used to conduct a comparative experiment among the proposed algorithms, i.e., the collaborative filtering algorithm based on the item and the collaborative filtering algorithm based on the item feature. The Mean Absolute Error (MAE) was utilized to conduct performance testing. The experimental results show that employing the proposed personalized recommendation algorithm based on the preference-feature would significantly improve the accuracy of evaluation predictions compared to two previous approaches.

With the popularity of Apps, the products in one domain become more and more similar to each other, and developers start to find the break from other domains. However, facing the large-scale data resource in App stores, it is difficult to identify the related domains, let alone gain useful features from the products in them. In this paper, we propose an approach to help developers learn information of features related to their App from the products in different domains. Firstly, we provide the method to extract features as well as their relationships from App descriptions to describe one domain. Then, the similar features shared by different domains are identified as the bridges for searching the potential information which may be re-used by the developers. Finally, we provide the framework of an interactive recommendation system to let developers gain and understand the information easily. To evaluate our approach, we conducted experiments based on the dataset on Google Play. The results show that the average precision of our approach for finding similar features between different domains can reach 82.38%, and the survey on developers indicate that the information recommended by our approach is useful for updating Apps and inspiring developers generate innovative ideas. INDEX TERMS App store mining, feature recommendation, cross-domain analysis, App evolution.

This paper improves the learner model of the original remote Chinese teaching platform of self-regulated learning, and in the new model, we used the GCA algorithm to takes place the original birch algorithm on learners' model. The improved learner model made a deep division for the learning styles of remote learners, which provides favorable theoretical basis for clustering algorithm.