A Hybrid Approach Based on Collaborative Filtering to Recommending Mobile Apps

A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories

“…To overcome data sparseness, model-based recommendation methods have been proposed. The most popular method is based on matrix factorization recommendation [2], [4], [5], [9]- [12], [16], [17], [19]- [21], [23], [25]- [27], [30], [34], which decomposes the user-app, M =R m * n two-dimensional matrix into two M 1 =R m * k , M 2 =R k * n low-dimensional matrices. The two low-dimensional vectors are multiplied to obtain the similarity matrix of the original matrix, and the elements of the similarity matrix represent the user's preference for an app.…”

“…[5] considered the effect of an app's version information on recommendation and proposed a method to improve recommendation results by incorporating the app's version information into the user-app matrix calculation. [9] proposed a hybrid matrix factorization method that considers an app's features and the app user's usage information. [10] incorporated the user's app usage and download behavior information and the app's popularity information into the user-app matrix calculation.…”

Incorporating contextual information into personalized mobile applications recommendation

“…The main idea of an app recommendation method is based on collaborative filtering recommendation, i.e., to find similar users of the target user and recommend the apps of the similar users to the target user. For example, [26] constructed a user-app feature matrix, used the matrix factorization method to obtain the app factor, and then constructed an app similarity model to recommend apps for target users. [22] analyzed the sentence structure, semantics and sentiment of the user's comment information on the app so that users can be classified.…”