With the increasing diversity of mobile apps, users install many apps in their smartphones and often use several apps together to meet a specific requirement. Because of the evolution of user habits and app functions, the set of apps using at the same time, i.e., app usage context, may change over time, which represents the dynamic correlation of different apps and even the evolution trend of the whole app ecosystem. Therefore, understanding how an apps usage context changes over time is very meaningful. In this paper, based on a seven-year app usage dataset, we explore the long-term app usage context dynamics and understand the underlying reasons and influence factors behind. Specifically, we build app co-occurrence graphs in different periods and learn app embeddings accordingly by leveraging graph embedding algorithm. We then measure the change of app usage context by the distance between neighboring app embeddings. As for the whole app ecosystem, we find that the change rate of app usage context undergoes up and down phrases, and varies in different app-categories. Furthermore, we explore three influence factors correlated with such dynamics. These results will be helpful for stakeholders to better understand the evolution of mobile users app usage behavior.

In recent years, due to the prevalence of online textual advertisements, increasing businesses recognize their huge potential in product promotion. The high-quality textual content has been empirically shown to have a substantial impact on consumers’ attitudes and decisions. As a result, persuasive tactics play an essential role in online textual advertisements, which are employed to increase the attractiveness, and sequentially increase the conversion rate and sales volume. As the context of persuasion, product attributes, e.g., category and price, also greatly influence the persuasion outcomes. However, they are largely overlooked by existing works. In this paper, we propose a novel framework to study context-aware persuasion by designing a multi-task learning model and performing extensive causal analysis. First, the prediction model recognizes the persuasive tactics employed in an advertising text and predicts their promotion effectiveness. Specifically, we design a disentangled representation learning algorithm to capture the persuasive tactics, and then develop a novel context-aware attention module to model the relationships between persuasive tactics and product attributes. Experiments on a large-scale real-world dataset demonstrate the superior performance of our proposed model over state-of-the-art baselines. Then we show its great practical value by conducting an in-depth causal analysis of context-aware results that our model learns, which offers insightful interpretations and guidelines for marketers to employ persuasive tactics in textual advertisements.

The wide adoption of mobile devices has provided us with a massive volume of human mobility records. However, a large portion of these records is unlabeled, i.e., only have GPS coordinates without semantic information (e.g., Point of Interest (POI)). To make those unlabeled records associate with more information for further applications, it is of great importance to annotate the original data with POIs information based on the external context. Nevertheless, semantic annotation of mobility records is challenging due to three aspects: the complex relationship among multiple domains of context, the sparsity of mobility records, and difficulties in balancing personal preference and crowd preference. To address these challenges, we propose CAP, a context-aware personalized semantic annotation model, where we use a Bayesian mixture model to model the complex relationship among five domains of context—location, time, POI category, personal preference, and crowd preference. We evaluate our model on two real-world datasets, and demonstrate that our proposed method significantly outperforms the state-of-the-art algorithms by over 11.8%.

Online services are playing critical roles in almost all aspects of users’ life. Users usually have multiple online identities (IDs) in different online services. In order to fuse the separated user data in multiple services for better business intelligence, it is critical for service providers to link online IDs belonging to the same user. On the other hand, the popularity of mobile networks and GPS-equipped smart devices have provided a generic way to link IDs, i.e., utilizing themobility tracesof IDs. However, linking IDs based on their mobility traces has been a challenging problem due to the highly heterogeneous, incomplete and noisy mobility data across services. In this paper, we proposeDPLink, an end-to-end deep learning based framework, to complete the user identity linkage task for heterogeneous mobility data collected from different services with different properties.DPLinkis made up by afeature extractorincluding a location encoder and a trajectory encoder to extract representative features from trajectory and acomparatorto compare and decide whether to link two trajectories as the same user. Particularly, we propose a pre-training strategy with a simple task to train theDPLinkmodel to overcome the training difficulties introduced by the highly heterogeneous nature of different source mobility data. Besides, we introduce a multi-modal embedding network and a co-attention mechanism inDPLinkto deal with the low-quality problem of mobility data. By conducting extensive experiments on two real-life ground-truth mobility datasets with eight baselines, we demonstrate thatDPLinkoutperforms the state-of-the-art solutions by more than 15 percent in terms of hit-precision. Moreover, it is expandable to add external geographical context data and works stably with heterogeneous noisy mobility traces.

The phenomenal success of the newly-emerging social e-commerce has demonstrated that utilizing social relations is becoming a promising approach to promote e-commerce platforms. In this new scenario, one of the most important problems is to predict the value of a community formed by closely connected users in social networks due to its tremendous business value. However, few works have addressed this problem because of 1) its novel setting and 2) its challenging nature that the structure of a community has complex effects on its value. To bridge this gap, we develop a Multi-scale Structure-aware Community value prediction network (MSC) that jointly models the structural information of different scales, including peer relations, community structure, and inter-community connections, to predict the value of given communities. Specifically, we first proposed a Masked Edge Learning Graph Convolutional Network (MEL-GCN) based on a novel masked propagation mechanism to model peer influence. Then, we design a Pair-wise Community Pooling (PCPool) module to capture critical community structures. Finally, we model the inter-community connections by distinguishing intra-community edges from inter-community edges and employing a Multi-aggregator Framework (MAF). Extensive experiments on a large-scale real-world social e-commerce dataset demonstrate our method’s superior performance over state-of-the-art baselines, with a relative performance gain of 11.40%, 10.01%, and 10.97% in MAE, RMSE, and NRMSE, respectively. Further ablation study shows the effectiveness of our designed components. Our code and dataset are available1.

Understanding human mobility benefits numerous applications such as urban planning, traffic control, and city management. Previous work mainly focuses on modeling spatial and temporal patterns of human mobility. However, the semantics of trajectory are ignored, thus failing to model people's motivation behind mobility. In this paper, we propose a novel semantics-aware mobility model that captures human mobility motivation using large-scale semantic-rich spatial-temporal data from location-based social networks. In our system, we first develop a multimodal embedding method to project user, location, time, and activity on the same embedding space in an unsupervised way while preserving original trajectory semantics. Then, we use hidden Markov model to learn latent states and transitions between them in the embedding space, which is the location embedding vector, to jointly consider spatial, temporal, and user motivations. In order to tackle the sparsity of individual mobility data, we further propose a von Mises-Fisher mixture clustering for user grouping so as to learn a reliable and fine-grained model for groups of users sharing mobility similarity. We evaluate our proposed method on two large-scale real-world datasets, where we validate the ability of our method to produce high-quality mobility models. We also conduct extensive experiments on the specific task of location prediction. The results show that our model outperforms state-of-the-art mobility models with higher prediction accuracy and much higher efficiency.

The prevalence of smartphones has promoted the popularity of mobile apps in recent years. Although significant effort has been made to understand mobile app usage, existing studies are based primarily on short-term datasets with limited time span, e.g., a few months. Therefore, many basic facts about the long-term evolution of mobile app usage are unknown. In this paper, we study how mobile app usage evolves over a long-term period. We first introduce an app usage collection platform named carat, from which we have gathered app usage records of 1,465 users from 2012 to 2017. We then conduct the first study on the long-term evolution processes on a macro-level, i.e., app-category, and micro-level, i.e., individual app. We discover that, on both levels, there is a growth stage enabled by the introduction of new technologies. Then there is a plateau stage caused by high correlations between app categories and a pareto effect in individual app usage, respectively. Additionally, the evolution of individual app usage undergoes an elimination stage due to fierce intra-category competition. Nevertheless, the diverseness of app-category and individual app usage exhibit opposing trends: app-category usage assimilates while individual app usage diversifies. Our study provides useful implications for app developers, market intermediaries, and service providers.

Understanding human mobility benefits numerous applications such as urban planning, traffic control and city management. Previous work mainly focuses on modeling spatial and temporal patterns of human mobility. However, the semantics of trajectory are ignored, thus failing to model people's motivation behind mobility. In this paper, we propose a novel semantics-aware mobility model that captures human mobility motivation using large-scale semantics-rich spatial-temporal data from location-based social networks. In our system, we first develop a multimodal embedding method to project user, location, time, and activity on the same embedding space in an unsupervised way while preserving original trajectory semantics. Then, we use hidden Markov model to learn latent states and transitions between them in the embedding space, which is the location embedding vector, to jointly consider spatial, temporal, and user motivations. In order to tackle the sparsity of individual mobility data, we further propose a von Mises-Fisher mixture clustering for user grouping so as to learn a reliable and fine-grained model for groups of users sharing mobility similarity. We evaluate our proposed method on two large-scale real-world datasets, where we validate the ability of our method to produce high-quality mobility models. We also conduct extensive experiments on the specific task of location prediction. The results show that our model outperforms state-of-the-art mobility models with higher prediction accuracy and much higher efficiency.