Li Kuang

Identifying Core Users Based on Trust Relationships and Interest Similarity in Recommender System

With the rapid development of Internet, the explosive growth of information challenges peoples capability on finding out items fitting to their own interests. The emergence of recommender system helps users to make decisions to a certain degree. So far, most of the studies pay much attention to designing or improving recommendation algorithms. However, few works consider the extraction of core users with whom recommender systems can generate satisfactory recommendation. In this paper, we propose new approaches to identifying core users based on trust relationships and interest similarity. The trust degree and interest similarity between all user pairs are calculated and sorted first, and two strategies based on frequency and weight of location are used to select core users. Experiments show the effectiveness of the extraction of core users and prove that 20% of core users enable recommender systems to achieve more than 90% of the accuracy of the top-N recommendation.

TMR: Towards an efficient semantic-based heterogeneous transportation media big data retrieval

Abstract In media retrieval system for intelligent transportation, media data variety and heterogeneity have been one of the most critical features. Documents with different formats may express similar semantic information, thus, searching documents reflecting users׳ intention has been a crucial and important task. For solving this problem, this paper proposes a novel semantic-based heterogeneous transportation media retrieval (TMR) approach to improve the performance. TMR supports the function of retrieving various media types such as image, video, audio and text by using a single media type. Firstly, semantic fields are extracted from the user annotating and automatic learning to express the users׳ intention. Secondly, ontology is used to represent the semantic fields of a media, and the ontology represented semantic information is saved together with the media document data. Thirdly, the semantic field adjustment process is described. Finally, fuzzy matching is employed to measure the similarity between the users׳ intention and media documents. For the returned results, we carry out the performance evaluation models in comparison with the existing approaches. Experimental result indicates the superiority of TMR in term of precision rate, computing speed, storage cost and user experience.

Combined retrieval

In Internet image retrieval, returned results may fail to satisfy the retrieval intentions of users because of noisy annotations. Solving the ambiguity in image retrieval by combining text features and visual information has been a challenging problem. In this paper, we propose a convenient and precise approach for Internet image retrieval called combined retrieval (CR), which costs minimized extra feedback to retrieve more results reflecting the query intentions of users. CR is used as a plug-in to commercial image search engines, such as Google and Bing, which are defined as host image search engines (HISE). First, in the returned result from HISE, document analysis is utilized to construct the image categories based on the Wikipedia categorical index. Returned images will be automatically categorized, and a convenient interface is provided for user feedback. Second, we describe the re-retrieval algorithm in which image data combined with particular text information will be sent to the HISE for re-retrieval. Finally, a perceptual hash based re-rank algorithm to optimize the returned images is proposed. Experimental results indicate that CR can significantly improve the retrieval performance with minimum effort and can provide a notably convenient user experience.

An Improved Privacy-Preserving Framework for Location-Based Services Based on Double Cloaking Regions with Supplementary Information Constraints

With the rapid development of location-based services in the field of mobile network applications, users enjoy the convenience of location-based services on one side, while being exposed to the risk of disclosure of privacy on the other side. Attacker will make a fierce attack based on the probability of inquiry, map data, point of interest (POI), and other supplementary information. The existing location privacy protection techniques seldom consider the supplementary information held by attackers and usually only generate single cloaking region according to the protected location point, and the query efficiency is relatively low. In this paper, we improve the existing LBSs system framework, in which we generate double cloaking regions by constraining the supplementary information, and then k-anonymous task is achieved by the cooperation of the double cloaking regions; specifically speaking, k dummy points of fixed dummy positions in the double cloaking regions are generated and the LBSs query is then performed. Finally, the effectiveness of the proposed method is verified by the experiments on real datasets.

A privacy-preserving multimedia recommendation in the context of social network based on weighted noise injection

With the popularity of social networks such as Facebook and Twitter, more information such as individual’s social connections is considered to make personalized multimedia recommendation, compared to traditional approaches based on the rating matrix. However, the massive data information used for recommendation often contains much personal privacy information. Once the information is obtained by attackers, user’s privacy will be revealed directly or indirectly. This paper proposes a privacy preserving method based on weighted noise injection technique to address the issue of multimedia recommendation in the context of social networks. More specifically, first, we extract core users from entire users. The extracted core users can represent the features of all users adequately. Only the relevant data of core users are then used for rating prediction. Second, we inject different noises to the rating matrix of core users according to different relations between the target user and core users. Third, we use the perturbed matrix to predict the ratings of unused multimedia resources for the target user based on a mixed collaborative filtering approach. By comparing with the traditional noise injection method, the experimental results show that the proposed approach can get better performance of privacy preserving multimedia recommendation.

CASP: A Context-Aware Transparent Active Service Provision Architecture in a Mobile Internet Environment

Driven by the development of the mobile Internet and the emergence of heterogeneous mobile devices, context-aware applications are attracting growing interest. Based on the idea of transparent computing, the authors propose a novel context-aware service provision (CASP) architecture to transparently and actively provide suitable services to clients. In this article, they present the system model and describe a series of key technologies in CASP, including the client parameter acquisition scheme, the user behavior analysis approach, the service choice algorithm, and the transmission optimization method. Based on the established architecture, they developed software and ran a performance evaluation, proving that CASP offers a good user experience and outstanding generality.

Predicting Short-Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment

With the rapid development of IoT, the disadvantages of Cloud framework have been exposed, such as high latency, network congestion, and low reliability. Therefore, the Fog Computing framework has emerged, with an extended Fog Layer between the Cloud and terminals. In order to address the real-time prediction on electricity demand, we propose an approach based on XGBoost and ARMA in Fog Computing environment. By taking the advantages of Fog Computing framework, we first propose a prototype-based clustering algorithm to divide enterprise users into several categories based on their total electricity consumption; we then propose a model selection approach by analyzing users’ historical records of electricity consumption and identifying the most important features. Generally speaking, if the historical records pass the test of stationarity and white noise, ARMA is used to model the user’s electricity consumption in time sequence; otherwise, if the historical records do not pass the test, and some discrete features are the most important, such as weather and whether it is weekend, XGBoost will be used. The experiment results show that our proposed approach by combining the advantage of ARMA and XGBoost is more accurate than the classical models.

A Personalized QoS Prediction Approach for CPS Service Recommendation Based on Reputation and Location-Aware Collaborative Filtering

With the rapid development of cyber-physical systems (CPS), building cyber-physical systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedure of building Cyber-physical systems, it has been found that a large number of functionally equivalent services exist, so it becomes an urgent task to recommend suitable services from the large number of services available in CPS. However, since it is time-consuming, and even impractical, for a single user to invoke all of the services in CPS to experience their QoS, a robust QoS prediction method is needed to predict unknown QoS values. A commonly used method in QoS prediction is collaborative filtering, however, it is hard to deal with the data sparsity and cold start problem, and meanwhile most of the existing methods ignore the data credibility issue. Thence, in order to solve both of these challenging problems, in this paper, we design a framework of QoS prediction for CPS services, and propose a personalized QoS prediction approach based on reputation and location-aware collaborative filtering. Our approach first calculates the reputation of users by using the Dirichlet probability distribution, so as to identify untrusted users and process their unreliable data, and then it digs out the geographic neighborhood in three levels to improve the similarity calculation of users and services. Finally, the data from geographical neighbors of users and services are fused to predict the unknown QoS values. The experiments using real datasets show that our proposed approach outperforms other existing methods in terms of accuracy, efficiency, and robustness.

A Privacy Protection Model of Data Publication Based on Game Theory

With the rapid development of sensor acquisition technology, more and more data are collected, analyzed, and encapsulated into application services. However, most of applications are developed by untrusted third parties. Therefore, it has become an urgent problem to protect users’ privacy in data publication. Since the attacker may identify the user based on the combination of user’s quasi-identifiers and the fewer quasi-identifier fields result in a lower probability of privacy leaks, therefore, in this paper, we aim to investigate an optimal number of quasi-identifier fields under the constraint of trade-offs between service quality and privacy protection. We first propose modelling the service development process as a cooperative game between the data owner and consumers and employing the Stackelberg game model to determine the number of quasi-identifiers that are published to the data development organization. We then propose a way to identify when the new data should be learned, as well, a way to update the parameters involved in the model, so that the new strategy on quasi-identifier fields can be delivered. The experiment first analyses the validity of our proposed model and then compares it with the traditional privacy protection approach, and the experiment shows that the data loss of our model is less than that of the traditional k-anonymity especially when strong privacy protection is applied.

Extracting Core Users Based on Features of Users and Their Relationships in Recommender Systems

As an effective way to solve information overload, recommender system has drawn attention of scholars from various fields. However, existing works mainly focus on improving the accuracy of recommendation by designing new algorithms, while the different importance of individual users has not been well addressed. In this paper, the authors propose new approaches to identifying core users based on trust relationships and interest similarity between users, and the popular degree, trust influence and resource of individual users. First, the trust degree and interest similarity between all user pairs, as well as the three attributes of individuals are calculated. Second, a global core user set is constructed based on three strategies, which are frequency-based, rank-based, and fusion-sorting-based. Finally, the authors compare their proposed methods with other existing methods from accuracy, novelty, long-tail distribution and user degree distribution. Experiments show the effectiveness of the authors core user extraction methods.