## 6 Conclusions  
In conclusion, we propose a graph-based representation model in this paper that greatly improves location recommendation results with app usage records and solves the data sparsity problem. Firstly, we analyze the data sparsity problem in personalized location recommendations and discuss the feasibility of app usage records. Then, the representation model with attributed bipartite graph is introduced in detail. The experiment results show that compared with the state-of-art models, our proposed model has the best recommendation performance under different data sparsity levels. Especially, when facing a severe sparsity problem, the performance of our model has greater improvements due to the aggregations of user-app-location interactions. Additionally, it also further confirms the strong correlation between app data usage, user interests and location characteristics. This is a successful attempt to discover deep representations of users and locations via attributed graph-based network. To sum up, the main contributions of our paper are summarized as follows:  
1. To the best of our knowledge, it is the first to solve the data sparsity problem in location recommendations by aggregating user-app-location associations, which can also inspire the research works about users' app usage behavior. We innovatively introduce app usage records as complementary information, in which both users' habits and location features are revealed. This method effectively alleviates the data sparsity problem and greatly improves the recommendation performances. 2. A graph-based representation model is proposed to learn both users' and locations' latent representations from an attributed bipartite graph. Our model explicitly uses associations of user-app-location, and captures various high-order features due to the information propagation and aggregation in graph structure. Therefore, it can significantly improve location recommendations, even under the circumstances of severe data sparsity. 3. Adequate experiments are conducted on two real-life datasets to show the superior and stable performance of our proposed model. Our model achieves the best performance compared with the state-of-art methods. It also works well under severe data sparsity, which has a higher increase in recommending performances when facing higher sparsity. For example, in Telecom dataset, when the data sparsity is 30%, _Top3 Hitrate_, _Top3 Accuracy_, and _nDCG3_ of our model are 11.42%, 8.19%, and 6.07% higher than the best baseline model, respectively. While data sparsity is 70%, _Top3 Hitrate_, _Top3 Accuracy_, and _nDCG3_ of our model achieve performances at least 21.76%, 14.47%, and 14.00% higher than the best baseline model, respectively. In practical application, our proposed model can be employed to improve user experiences, user profile extraction and user app behaviors extraction, etc. However, there are still limitations in this paper, such as not considering long-term factors (i.e., user preferences changing) and short-term time affects (i.e., specific time points in a day). In future work, these temporal features will be taken into consideration and multi-head attention structure  
Figure 7: Performances of Different Layers on Two Datasets. (**a**) _Telecom Dataset_. (**b**) _Talking Dataset_. can be applied to discover more associations among users, locations and apps, which can improve recommendation results. Conceptualization, X.C. and J.C.; methodology, X.C. and J.C.; software, J.C. validation, X.C., J.C. and X.L. ; formal analysis, X.C. and J.C.; investigation, W.M. ; writing original draft preparation, J.C.; writing, review and editing, X.C., J.C. and X.L. ; visualization, J.C. and W.M. ; supervision, X.C. and W.M. ; project administration, X.C. and X.L. ; funding acquisition, X.C. and X.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Key Research and Development Program of China (No. 2019 YFE0196400), Guangdong R&D Project in Key Areas under Grant (No.