As one of the cyber–physical–social systems that plays a key role in people's daily activities, a smart city is producing a considerable amount of industrial data associated with transportation, healthcare, business, social activities, and so on. Effectively and efficiently fusing and mining such data from multiple sources can contribute much to the development and improvements of various smart city applications. However, the industrial data collected from the smart city are often sensitive and contain partial user privacy such as spatial–temporal context information. Therefore, it is becoming a necessity to secure user privacy hidden in the smart city data before these data are integrated together for further mining, analyses, and prediction. However, due to the inherent tradeoff between data privacy and data availability, it is often a challenging task to protect users’ context privacy while guaranteeing accurate data analysis and prediction results after data fusion. Considering this challenge, a novel privacy-aware data fusion and prediction approach for the smart city industrial environment is put forward in this article, which is based on the classic locality-sensitive hashing technique. At last, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results show better prediction performances of our approach compared to other competitive ones.

Privacy-Aware Data Fusion and Prediction With Spatial-Temporal Context for Smart City Industrial Environment

Cyber-physical systems (CPS) have received much attention from both academia and industry. An increasing number of functions in CPS are provided in the way of services, which gives rise to an urgent task, that is, how to recommend the suitable services in a huge number of available services in CPS. In traditional service recommendation, collaborative filtering (CF) has been studied in academia, and used in industry. However, there exist several defects that limit the application of CF-based methods in CPS. One is that under the case of high data sparsity, CF-based methods are likely to generate inaccurate prediction results. In this paper, we discover that mining the potential similarity relations among users or services in CPS is really helpful to improve the prediction accuracy. Besides, most of traditional CF-based methods are only capable of using the service invocation records, but ignore the context information, such as network location, which is a typical context in CPS. In this paper, we propose a novel service recommendation method for CPS, which utilizes network location as context information and contains three prediction models using random walking. We conduct sufficient experiments on two real-world datasets, and the results demonstrate the effectiveness of our proposed methods and verify that the network location is indeed useful in QoS prediction.

https://www.mdpi.com/1424-8220/17/9/2059/pdf

Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems.

https://uwe-repository.worktribe.com/preview/4918315/Info%20Sci.pdf

Spatial-temporal data-driven service recommendation with privacy-preservation

Mobile Service selection is an important but challenging problem in service and mobile computing. Quality of service (QoS) predication is a critical step in service selection in 5G network environments. The traditional methods, such as collaborative filtering (CF), suffer from a series of defects, such as failing to handle data sparsity. In mobile network environments, the abnormal QoS data are likely to result in inferior prediction accuracy. Unfortunately, these problems have not attracted enough attention, especially in a mixed mobile network environment with different network configurations, generations, or types. An ensemble learning method for predicting missing QoS in 5G network environments is proposed in this paper. There are two key principles: one is the newly proposed similarity computation method for identifying similar neighbors; the other is the extended ensemble learning model for discovering and filtering fake neighbors from the preliminary neighbors set. Moreover, three prediction models are also proposed, two individual models and one combination model. They are used for utilizing the user similar neighbors and servicing similar neighbors, respectively. Experimental results conducted in two real-world datasets show our approaches can produce superior prediction accuracy.

Collaborative Service Selection via Ensemble Learning in Mixed Mobile Network Environments

Web service recommendation is one of the key problems in service computing, especially in the case of a large number of service candidates. The QoS (quality of service) values are usually leveraged to recommend services that best satisfy a user’s demand. There are many existing methods using collaborative filtering (CF) to predict QoS missing values, but very limited works can leverage the network location information in the user side and service side. In real-world service invocation scenario, the network location of a user or a service makes great impact on QoS. In this paper, we propose a novel collaborative recommendation framework containing three novel prediction models, which are based on two techniques, i.e. matrix factorization (MF) and network location-aware neighbor selection. We first propose two individual models that have the capability of using the user and service information, respectively. Then we propose a unified model that combines the results of the two individual models. We conduct sufficient experiments on a real-world dataset. The experimental results demonstrate that our models achieve higher prediction accuracy than baseline models, and are not sensitive to the parameters.

QoS Prediction for Web Service Recommendation with Network Location-Aware Neighbor Selection

Context-Aware Recommender System (CARS) aims to not only recommend services similar to those already rated with the highest score, but also provide opportunities for exploring the important role of temporal, spatial and social contexts for personalized web services recommendation. A key step for temporal-based CARS methods is to explore the time decay process of past invocation records to make the Quality of Services (QoS) prediction. However, it is a nontrivial task to model the temporal effects on web services recommendation, due to the dynamic features of contextual information in view of temporal spatial correlations. For instance, in location-aware services recommendation, the user's geographical position would change very frequently as time goes on. In this paper, we propose a Context-Aware Services Recommendation based on Temporal Effectiveness (CASR-TE) method. Inspired by existing time decay approaches, we first present an enhanced temporal decay model combining the time decay function with traditional similarity measurement methods. Then, we model temporal spatial correlations as well as their impacts on the user preference expansion. Finally, we evaluate the CASR-TE method on WS-Dream dataset by evaluation matrices of both RMSE and MAE. Experimental results show that our approach outperforms several benchmark methods with a significant margin.

Modeling Temporal Effectiveness for Context-Aware Web Services Recommendation

Recent years have witnessed the growing research interest in the Context-Aware Recommender System (CARS). CARS for Web service provides opportunities for exploring the important role of temporal and spatial contexts, separately. Although many CARS approaches have been investigated in recent years, they do not fully address the potential of temporal-spatial correlations in order to make personalized recommendation. In this paper, the Context-Aware Services Recommendation based on Temporal-Spatial Effectiveness (named CASR-TSE) method is proposed. We first model the effectiveness of spatial correlations between the user's location and the service's location on user preference expansion before the similarity computation. Second, we present an enhanced temporal decay model considering the weighted rating effect in the similarity computation to improve the prediction accuracy. Finally, we evaluate the CASR-TSE method on a real-world Web services dataset. Experimental results show that the proposed method significantly outperforms existing approaches, and thus it is much more effective than traditional recommendation techniques for personalized Web service recommendation.

CASR-TSE: Context-Aware Web Services Recommendation for Modeling Weighted Temporal-Spatial Effectiveness

Context-Aware Recommender System aims to recommend items not only similar to those already rated with the highest score, but also that could combine the contextual information with the recommendation process. Existing context-aware Web services recommendation methods directly use context as a "filter" to discard services that may conflict with the current user's preference. However, the discarded services may be valuable for another user or for the same user under a new context, as one man's trash may be another's treasure. We assume that failing to handle the contextual reasons behind the user preference may introduce inaccurate recommendation, and even significant biases in recommendation. In this work, we propose a novel method dubbed CASR-UP, which aims to exploit the contextual factors of the user preference to improve Quality of Service (QoS) prediction and services recommendation accuracy. Our method consists of three stages: 1) context-aware similarity mining to get the set of users having similar context with the current user, 2) data filtering based on user preference in current context so as to get the invocation records of the services corresponding to the current user's preference, 3) Web services QoS prediction, recommendation and evaluation by Bayesian Inference. Experimental results on WS-Dream dataset is evaluated by both RMSE and MAE. The results show the proposed method improves prediction accuracy and outperforms the compared methods.

Context-Aware Web Services Recommendation Based on User Preference

The invention discloses a method for recommending context-aware Web service on the basis of weighted time-space effects. The method includes building weighted time decay models so as to find user sets with preference similar to preference of current users under condition that time decay effects are considered; acquiring user sets in contexts similar to current contexts of the users by the aid of location-aware similarity mining algorithms; building relation models of time-space correlation and user preference so as to obtain network service call records conforming to the current preference of the users; predicting QoS (quality of service) values of service by the aid of Bayes theorems on the basis of the obtained user sets with the preference similar to the preference of the current users under the condition that the time decay effects are considered, the user sets in the contexts similar to the current contexts of the users and the network service call records conforming to the current preference of the users so as to obtain the service with the highest degrees of satisfaction; evaluating outcome of the obtained service. The method has the advantage that the recommendation accuracy can be improved by the aid of the method.

Method for recommending context-aware Web service on basis of weighted time-space effects

In this paper, we propose a novel ubiquitous Web service recommendation approach to context-aware recommendation based on user location update (CASR-ULU). First, we model the influence of user location update based on user preference expansion. Second, we perform the context-aware similarity mining for updated location. Third, we predict the Quality of Service by Bayesian inference, and thus recommend the ideal Web service for the specific user subsequently. Furthermore, a calendar Android mobile application is implemented to testify the CASR-ULU algorithm in a ubiquitous environment. Finally, we evaluate the CASR-ULU method on WS-Dream dataset with evaluation matrices such as RMSE and MAE. Experimental results show that our method achieves competitive recommendation performance in a ubiquitous environment, compared to several state-of-the-art methods.

Yakun Hu

Papers

Modeling Temporal Effectiveness for Context-Aware Web Services Recommendation

CASR-TSE: Context-Aware Web Services Recommendation for Modeling Weighted Temporal-Spatial Effectiveness

Context-Aware Web Services Recommendation Based on User Preference

Method for recommending context-aware Web service on basis of weighted time-space effects

Context-Aware Ubiquitous Web Services Recommendation Based on User Location Update