Next mobile app recommendation aims to recommend the next app that a user is most likely to use based on the user’s app usage behaviors, which is beneficial for improving user experience, app pre-loading, and system optimization. However, existing works ignore the complex correlations between apps in the app usage sessions. In addition, they do not consider the dynamics of user interests over time. To address these concerns, we propose a novel model named dynamic usage graph network (DUGN) to recommend the next app that a user is most likely to use. To model the complex correlations among apps explicitly, we adopt the dynamic graph structure to learn the dynamics of user interests. Firstly, we extract user interests in each app usage graph by using the hierarchical graph attention mechanism. Secondly, we capture user interests evolving over time, and generate the dynamic user embeddings by modeling the temporal dependencies among multiple app usage graphs. Finally, we obtain the current user interests in the current app usage graph, fuse multiple user interests and generate comprehensive user embeddings for next mobile app recommendation. We conduct experiments on real-world datasets. The results show that our model outperforms the state-of-art recommendation methods.

With the growth of Internet of Things (IoT) devices, smart travel methods, such as sharing-bike and ride-hailing become popular commuting methods. With people’s growing needs and the rapid dynamics in a city environment, simply using a single travel demand for prediction may be insufficient. Alternatively, modeling multiple travel demands simultaneously can deepen our understanding toward the status of these potentially correlated demands and deploy the transportation in the city better. An important observation in this work is that multiple travel demands in a city often show common patterns, referred to as the shared mobility-aware knowledge. In addition, there are also unique patterns that characterize individual travel demand resulting in unique knowledge. To better leverage the shared and unique knowledge, we propose a novel framework (MultiST) to predict multiple spatial–temporal sequences (multiple travel demands) via two components that extract the shared and unique spatial–temporal dependencies, respectively. For the unique component, we use convolutional neural networks and gated recurrent units to embed unique knowledge. For the shared component, we design a recurrent Gaussian cell to extract temporal dependencies. Empirical results show that MultiST outperforms six state-of-the-art baseline methods and three variants of MultiST. We further visualize the temporal dependencies of the shared knowledge and discuss the practical implications.

App survival prediction is a significant task in mobile service development. It differs from existing prediction tasks in two aspects. First, rather than the traditional survival prediction in bioinformatics where all the patients’ survival probabilities decay in a similar way, apps’ survival pattern varies from each other. Second, affected by multiple factors, an app's popularity is time-varying and sequence-dependent, which makes existing short-term prediction methods not applicable due to error accumulation. These characteristics bring great difficulties in app survival prediction. In this paper, we propose AppLife, a framework that fuses multi-source influence factors and utilizes Multi-Task Learning (MTL) to combine the state information of mobile app for survival prediction. First, we analyze how the app survival is affected by multi-source factors, including download history, ratings, and reviews. Second, to overcome error accumulation in long-term prediction, we propose a novel MTL based approach. The approach estimates whether an app is surviving at each time interval during the life cycle of apps and leverages relatedness among tasks to improve the prediction performance. Last, we collect a large-scale dataset with more than 35,000 apps, based on which we evaluate our proposed framework and results show that it outperforms the seven state-of-the-art methods.

The cross-domain recommender systems aim to alleviate the data sparsity problem in a target domain by transferring knowledge from a source domain. However, existing works ignore the latent information underlying the user-item interactions. In addition, they don’t explicitly model the intra-domain and cross-domain interactions. To address these concerns, we propose a novel model named cross-graph knowledge transfer network to improve the recommendation performance. To explicitly model intra-domain and cross-domain interactions, we utilize the graph structure to transfer knowledge across domains. Firstly, we design a neighbor sampling method to extract useful intra-domain and cross-domain interactions. Secondly, we aggregate multiple interactive information in each domain and generate intra-domain embeddings by using intra-domain attention mechanism. Thirdly, we fuse the information from two domains to generate effective user and item embeddings by using cross-domain attention mechanism. Finally, we feed user and item embeddings into the domain-specific prediction layers for personalized recommendation. We conduct experiments on real-world datasets. The results show that our model outperforms five state-of-art methods.

With the rapid development of mobile app ecosystem, mobile apps have grown greatly popular. The explosive growth of apps makes it difficult for users to find apps that meet their interests. Therefore, it is necessary to recommend user with a personalized set of apps. However, one of the challenges is data sparsity, as users’ historical behavior data are usually insufficient. In fact, user’s behaviors from different domains in app store regarding the same apps are usually relevant. Therefore, we can alleviate the sparsity using complementary information from correlated domains. It is intuitive to model users’ behaviors using graph, and graph neural networks have shown the great power for representation learning. In this article, we propose a novel model, Deep Multi-Graph Embedding (DMGE), to learn cross-domain app embedding. Specifically, we first construct a multi-graph based on users’ behaviors from different domains, and then propose a multi-graph neural network to learn cross-domain app embedding. Particularly, we present an adaptive method to balance the weight of each domain and efficiently train the model. Finally, we achieve cross-domain app recommendation based on the learned app embedding. Extensive experiments on real-world datasets show that DMGE outperforms other state-of-art embedding methods.

In recent years, bike-sharing systems have been widely deployed in many big cities, which provide an economical and healthy lifestyle. With the prevalence of bike-sharing systems, a lot of companies join the bike-sharing market, leading to increasingly fierce competition. To be competitive, bike-sharing companies and app developers need to make strategic decisions and predict the popularity of bike-sharing apps. However, existing works mostly focus on predicting the popularity of a single app, the popularity contest among different apps has not been explored yet. In this paper, we aim to forecast the popularity contest between Mobike and Ofo, two most popular bike-sharing apps in China. We develop CompetitiveBike, a system to predict the popularity contest among bike-sharing apps leveraging multi-source data. We extract two novel types of features: coarse-grained and fine-grained competitive features, and utilize Random Forest model to forecast the future competitiveness. In addition, we view mobile apps competition as a long-term event and generate the event storyline to enrich our competitive analysis. We collect data about two bike-sharing apps and two food ordering & delivery apps from 11 app stores and Sina Weibo, implement extensive experimental studies, and the results demonstrate the effectiveness and generality of our approach.

The popularity prediction of mobile apps provides substantial value to a broad range of applications, ranging from app development to targeted advertising. However, most previous studies do this work by establishing regression models for impact factors, or using clustering and classification algorithms. It does not fully investigate the process of popularity evolution and the reasons behind it. In this paper, we discuss and analyze the potential predictors, especially the impact of early evolutionary patterns on future popularity. To this end, we first explore six basic evolutionary patterns and six impact factors that are closely related to app popularity. After detailed analysis, we present CrowdPop, a popularity prediction model based on the Random Forest algorithm, to quantify patterns and factors as predictors of CrowdPop. The experiment results with a real-world dataset of 126 apps indicate that, compared with baseline methods, our CrowdPop performs better in mobile app popularity prediction.