A Broad Learning Approach for Context-Aware Mobile Application Recommendation

With the rapid development of mobile apps, the availability of a large number of mobile apps in application stores brings challenge to locate appropriate apps for users. Providing accurate mobile app recommendation for users becomes an imperative task. Conventional approaches mainly focus on learning users' preferences and app features to predict the user-app ratings. However, most of them did not consider the interactions among the context information of apps. To address this issue, we propose a broad learning approach for \textbf{C}ontext-\textbf{A}ware app recommendation with \textbf{T}ensor \textbf{A}nalysis (CATA). Specifically, we utilize a tensor-based framework to effectively integrate user's preference, app category information and multi-view features to facilitate the performance of app rating prediction. The multidimensional structure is employed to capture the hidden relationships between multiple app categories with multi-view features. We develop an efficient factorization method which applies Tucker decomposition to learn the full-order interactions within multiple categories and features. Furthermore, we employ a group $\ell_{1}-$norm regularization to learn the group-wise feature importance of each view with respect to each app category. Experiments on two real-world mobile app datasets demonstrate the effectiveness of the proposed method

The Rigorous Model for Similarity Transformation under Intra-frame and Inter-frame Covariance

The coordinates are obtained from observations by using least-squares method, so their precision should be contaminated by observation errors and the covariance also exists between common points and non-common points. The coordinate errors don't only exist in the initial frame but also in the target frame. But the classical stepwise approach for coordinate frame transformation usually takes the coordinate errors of the initial frame into account and overlooks the stochastic correlation between common points and non-common points. A new rigorous unified model is proposed for coordinate frame transformation that takes into account both the errors of all coordinates in both fame and inter-frame coordinate covariance between common points and non-common points, and the corresponding estimator for the transformed coordinates are derived and involve appropriate corrections to the standard approach, in which the transformation parameters and the transformed coordinates for all points are computed in a single-step least squares approach. The inter frame coordinate covariance should be consistent to their uncertainties, but in practice their uncertainties are not consistent. To balance the covariance matrices of both frames, a new adaptive estimator for the unified model is thus derived in which the corresponding adaptive factor is constructed by the ratio computed by Helmert covariance component estimation, reasonable and consistent covariance matrices are arrived through the adjustment of the adaptive factor. Finally, an actual experiments with 2000 points from the crustal movement observation network of China (abbreviated CMONOC) is carried out to verify the implement of the new model, the results show that the proposed model can significantly improve the precision of the coordinate transformation

A GNSS/5G Integrated Three-Dimensional Positioning Scheme Based on D2D Communication

The fifth generation (5G) communication has the potential to achieve ubiquitous positioning when integrated with a global navigation satellite system (GNSS). The device-to-device (D2D) communication, serving as a key technology in the 5G network, provides the possibility of cooperative positioning with high-density property. The mobile users (MUs) collaborate to jointly share the position and measurement information, which can make use of more references for positioning. In this paper, a GNSS/5G integrated three-dimensional positioning scheme based on D2D communication is proposed, where the time of arrival (TOA) and received signal strength (RSS) measurements are jointly utilized in the 5G network. The density spatial clustering of application with noise (DBSCAN) is exploited to reduce the position uncertainty of the cooperative nodes, and the positions of the requesting nodes are obtained simultaneously. The particle filter (PF) algorithm is further conducted to improve the position accuracy of the requesting nodes. Numerical results show that the position deviation of the cooperative nodes can be significantly decreased and that the proposed algorithm performs better than the nonintegrated one. The DBSCAN brings an increase of about 50% in terms of the positioning accuracy compared with GNSS results, and the PF further increases the accuracy about 8%. It is also verified that the algorithm suits the fixed and dynamic condition well

Semi-parametric adjustment model methods for positioning of seafloor control point

This paper focuses on solving the problem of seafloor control point absolute positioning with low vertical accuracy based on the survey ship sailing circle. The method of dealing with the systematic error based on semi-parametric adjustment model was proposed. Firstly, the influence of sound speed change on ranging error is analyzed. Secondly, a semi-parametric adjustment model for determining three-dimensional coordinates of underwater control points was established. And respectively proposed solutions under two different conditions, the observation duration is an integral multiple or non-integer multiple of the long-period term of the ranging error. Simulation experiment results show that this method can obviously improve the accuracy of vertical solution of seafloor control point compared with difference technique and least square method when internal waves exist and observation duration is less than an integer multiple of the long-period term of the ranging error

A GNSS/5G Integrated Three-Dimensional Positioning Scheme Based on D2D Communication

The fifth generation (5G) communication has the potential to achieve ubiquitous positioning when integrated with a global navigation satellite system (GNSS). The device-to-device (D2D) communication, serving as a key technology in the 5G network, provides the possibility of cooperative positioning with high-density property. The mobile users (MUs) collaborate to jointly share the position and measurement information, which can make use of more references for positioning. In this paper, a GNSS/5G integrated three-dimensional positioning scheme based on D2D communication is proposed, where the time of arrival (TOA) and received signal strength (RSS) measurements are jointly utilized in the 5G network. The density spatial clustering of application with noise (DBSCAN) is exploited to reduce the position uncertainty of the cooperative nodes, and the positions of the requesting nodes are obtained simultaneously. The particle filter (PF) algorithm is further conducted to improve the position accuracy of the requesting nodes. Numerical results show that the position deviation of the cooperative nodes can be significantly decreased and that the proposed algorithm performs better than the nonintegrated one. The DBSCAN brings an increase of about 50% in terms of the positioning accuracy compared with GNSS results, and the PF further increases the accuracy about 8%. It is also verified that the algorithm suits the fixed and dynamic condition well