Mobile Service Recommendation Research Articles

QoS prediction for smart service management and recommendation based on the location of mobile users

Quality of Service (QoS) directly reflects the degree to which services offered by providers satisfy the non-functional requirements of users. QoS information is not usually available as a priori to providers when recommending services to user queries, this creates uncertainty in offering right services to right queries. Recent researches in service recommendation and management mainly address the issues of sparse data prediction and user personalized recommendation. Recommendation systems require smart strategies of recommending and managing services in accordance with the user queries. Predicting the QoS requirements of user queries before recommending the services can potentially aid in offering the most suitable services to users. This paper proposes a hybrid mobile service recommendation and management model based on semantic recommendation along with location-based quality preference analysis for emerging 5G mobile networks. The proposed model can effectively predict the QoS by exploiting previously invoked services to identify the best matching mobile services based on the similarity between users and services. Performance evaluation based on a published web services dataset demonstrates an enhanced prediction accuracy with an effective reduction in time overheads when compared to other related methods.

Improved Personalized Recommendation Algorithm Based on Context-Aware in Mobile Computing Environment

With the development and popularization of e-commerce and Internet, more and more attention has been paid to personalized recommendation for users. The traditional user interest model only considers the user’s behavior on the project, ignoring the user’s context at that time. Pointing to the shortage that context-related factors are not considered in previous works, combining the characteristics of a mobile computing environment, this paper studies the algorithm and model of mobile service recommendation. A recommendation algorithm based on specified context filtering in mobile computing environment is proposed. The context of the classification is aggregated, by grouping the scenarios of the same category together. Through experiments, we found that the improved personalized recommendation algorithms are superior to the common collaborative filtering algorithm.

ON THE CHALLENGE OF SERVICE RECOMMENDATION TO MOBILE USERS IN SMART CITIES: CONTEXT AND ARCHITECTURE

Abstract. The industrial and academic interest of the research on mobile service recommendation systems based on a wide range of potential applications has significantly increased, owing to the rapid progress of mobile technologies. These systems aim to recommend the right product, service or information to the right mobile users at anytime and anywhere. In smart cities, recommending such services becomes more interesting but also more challenging due to the wide range of information that can be obtained on the user and his surrounding. This quantity and variety of information create problems in terms of processing as well as the problem of choosing the right information to use to offer services. We consider that to provide personalized mobile services in a smart city and know which information is relevant for the recommendation process, identifying and understanding the context of the mobile user is the key.This paper aims to address the issue of recommending personalized mobile services in smart cities by considering two steps: defining the context of the mobile user and designing an architecture of a system that can collect and process context data. Firstly, we propose an UML-based context model to show the contextual parameters to consider in recommending mobile services in a smart city. The model is based on three main classes from which others are divided: the user, his device and the environment. Secondly, we describe a general architecture based on the proposed context model for the collection and processing of context data.

Research on multi‐source mobile commerce service recommendation model of data fusion based on tree network

SummaryWith the rapid development of e‐commerce and computer technology, the recommendation service in business activities has no longer implemented by people, but by software technology. In general, the e‐commerce website recommendation service can have divided into two types: standardization and personalization. The design of the recommendation service should be as follows, the content presentation should meet the consumer's shopping needs; we need to provide different processing level information according to the consumer's cognitive level; appropriately select the expression and strengthen the credibility of the recommendation. This article proposes a multi‐source data fusion strategy of mobile commerce service recommendation for tree‐based networks. On this basis, the tree‐node relationship is stored through redundant data, thus achieving complete decoupling between tree‐type nodes and reaching the tree. The purpose is to have the efficient storage and access of structured data. For the multimedia mobile device is limited by hardware conditions, limited storage capacity, weak computing power, and so on, using client‐side cached partial data and on‐demand sequence request server data and other strategies, making full use of the hierarchical structure of tree structure to design efficient. The tree synchronization model enables efficient buffering and accessing of server data by mobile devices.

A User Profile Awareness Service Collaborative Recommendation Algorithm Under LBSN Environment

Nowadays, location-based social network (LBSN) has become one of the most popular applications with the rapid development of mobile Internet. However, due to the spatial and real-time properties, mobile service recommendation under LBSN environment faces too many challenges especially data sparsity problem. To tackle these challenges, a recommendation framework is proposed in this paper which has four layers defined as data collection layer, user profile modeling layer, information processing layer and recommendation feedback layer, respectively. Furthermore, the ISC-CF algorithm is implemented to integrate users’ interest profile, social influence and current location context to effectively overcome the data sparsity problem. Thus, the social influence is quantified by a modified measure way. Finally, a dynamic and personalized adjustment algorithm is built by using the users’ profile tracking and the current location context. The experiment results show that the algorithm proposed in this paper has significantly superior performance compared with the other baseline recommendation methods in both hometown area and out-of-town area.

From Reputation Perspective: A Hybrid Matrix Factorization for QoS Prediction in Location-Aware Mobile Service Recommendation System

With the great development of mobile services, the Quality of Services (QoS) becomes an essential factor to meet end users’ personalized requirement on the nonfunctional performance of mobile services. However, most of the QoS values in real cases are unattainable because a service user would only invoke some specific mobile services. Therefore, how to predict the missing QoS values and recommend high-quality services to end users becomes a significant challenge in mobile service recommendation research. Previous QoS prediction researches demonstrate that the nonfunctional performance of mobile services is closely related to users’ location information. However, most location-aware QoS prediction methods ignore the premise that the obtainable QoS values observed by different users in same location region would probably be untrustworthy, which will lead to inaccurate and unreliable prediction results. To make credible location-aware QoS prediction, we propose a hybrid matrix factorization method integrated location and reputation information (LRMF) to predict the unattainable QoS values. Our approach firstly cluster users into different locational region based on their geographical distribution, and then we compute users’ reputation to identify untrustworthy users in every locational region. Finally, the unknown QoS values can be predicted by integrating locational cluster information and users’ reputation into a hybrid matrix factorization model. Comprehensive experiments are conducted on a public QoS dataset which contains sufficient real-world service invocation records. The evaluation results indicate that our LRMF method can effectively reduce the impact of unreliable users on QoS prediction and make credible mobile service recommendation.

A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment

With the increasing popularity of service computing paradigm, tremendous resources or services are emerging rapidly on the Web, imposing heavy burdens on the service selection decisions of users. In this situation, recommendation (e.g., collaborative filtering) has been considered as one of the most effective ways to alleviate such burdens. However, in the mobile and edge environment, the service recommendation bases, i.e., historical service usage data are often generated from various mobile devices (e.g., Smartphone and PDA) and stored in different edge platforms. Therefore, effective collaboration between these distributed edge platforms plays an important role in the successful mobile service recommendation. Such a cross-platform collaboration process often faces the following two challenges. First, a platform is often reluctant to release its data to other platforms due to privacy concerns. Second, the collaboration efficiency is often low when the data in each platform update frequently. In view of these two challenges, we introduce MinHash, an instance of Locality-Sensitive Hashing (LSH), into service recommendation, and further put forward a novel privacy-preserving and scalable mobile service recommendation approach based on two-stage LSH, namedSerRectwo-LSH. Finally, extensive experiments are conducted on WS-DREAM, a real distributed service quality dataset, and the evaluation results demonstrate that both the service recommendation accuracy and the scalability have been significantly improved while privacy preservation is guaranteed.

Formal Logic Research in Mobile Service Recommendation Model Based on Situation Calculus

Aiming at the shortcomings of formal logic research in the domain of service recommendation, this paper proposes a mobile service recommendation model MSR-SC based on situation calculus. On the basis of the basic method and the general process of mobile service recommendation, the logical theory system of situation calculus is introduced. With the initial situation as the starting point, much situation in specific action domain, fluent domain and situation domain are dynamically evolved according to certain rules. Finally, in order to verify the logical validity of the model, a formal description of a mobile service recommendation instance using MSR-SC is given. As can be seen from the example, the model has logic validity and correctness.

Privacy-Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment

With the ever-increasing popularity of mobile computing technology, a wide range of computational resources or services (e.g., movies, food, and places of interest) are migrating to the mobile infrastructure or devices (e.g., mobile phones, PDA, and smart watches), imposing heavy burdens on the service selection decisions of users. In this situation, service recommendation has become one of the promising ways to alleviate such burdens. In general, the service usage data used to make service recommendation are produced by various mobile devices and collected by distributed edge platforms, which leads to potential leakage of user privacy during the subsequent cross-platform data collaboration and service recommendation process. Locality-Sensitive Hashing (LSH) technique has recently been introduced to realize the privacy-preserving distributed service recommendation. However, existing LSH-based recommendation approaches often consider only one quality dimension of services, without considering the multidimensional recommendation scenarios that are more complex but more common. In view of this drawback, we improve the traditional LSH and put forward a novel LSH-based service recommendation approach named SerRecmulti-qos, to protect users’ privacy over multiple quality dimensions during the distributed mobile service recommendation process.

Mobile cloud services recommendation: a soft set-based approach

Mobile Internet and Cloud Computing technologies facilitate the rapid development of mobile cloud services. Particularly, mobile cloud services recommendation has become increasingly important as services become increasingly prevalent over the Internet. Existing recommendation mechanisms mainly focus on Quality of Services (QoS). However, the continuous emerging of many mobile cloud services leads to a server issue on the information overload for users. Hence, how to recommend the suitable mobile cloud services to users for addressing this issue is important. To this end, this paper incorporates the unique property of soft set and creatively investigated the soft set-based mobile cloud service recommendation mechanism and then devised the corresponding recommendation algorithm.

Improving Smart Conference Participation Through Socially Aware Recommendation

This research addresses recommending presentation sessions at smart conferences to participants. We propose a venue recommendation algorithm, Socially-Aware Recommendation of Venues and Environments (SARVE). SARVE computes correlation and social characteristic information of conference participants. In order to model a recommendation process using distributed community detection, SARVE further integrates the current context of both the smart conference community and participants. SARVE recommends presentation sessions that may be of high interest to each participant. We evaluate SARVE using a real world dataset. In our experiments, we compare SARVE to two related state-of-the-art methods, namely: Context-Aware Mobile Recommendation Services (CAMRS) and Conference Navigator (Recommender) Model. Our experimental results show that in terms of the utilized evaluation metrics: precision, recall, and f-measure, SARVE achieves more reliable and favorable social (relations and context) recommendation results.

Online role mining for context-aware mobile service recommendation

Finding and recommending suitable services for mobile devices are increasingly important due to the popularity of mobile Internet. While recent research has attempted to use role-based approaches t...

Context Ontology Modeling for Mobile Petrol Station Recommendation

To support the personalized mobile recommendation service, a context modeling approach based on ontology was proposed. The role of context ontologies in mobile recommenders was discussed, and under the application background of petrol station recommendation, a two-level context ontology model was designed. The model contains an upper ontology to describe general concepts, and a domain ontology extended from the upper ontology. The context ontology provides formalizations representing the mainly entities including users, objects and contexts, and their interactive relationships in mobile recommendation environments. Finally, a use case of the instantiated context ontology was demonstrated.

Ubiquitous conference management system for mobile recommendation services based on mobilizing social networks: A case study of u-conference

In this paper, we propose a social network-based mechanism to be aware of user contexts and to provide contextually relevant mobile services to users. A social network among users is regarded as the channel for exchanging and propagating their contexts. To efficiently discover the contexts of a certain users the contexts of his neighbors can be fused to provide mobile recommendation service to mobile subscribers. However, since the social network of a user has been fragmented into more than one, it is difficult to put the number of contexts from the fragmented social networks together (Jung, 2009b). Thereby, we mobilize all possible on- and off-line social networks to build an ego-centric social network. We have implemented the proposed system, which is called u-conference system, by collecting the social network dataset from online (e.g., Facebook, Twitter, CyWorld, and co-authoring patterns in major Korean journals) and offline (e.g., co-participation patterns in a number of Korean domestic conferences). Once we have implemented the system, we have provided mobile services to the conference participants by sending text messages about time schedule of relevant presentations.

Contextualized mobile recommendation service based on interactive social network discovered from mobile users

Personal context is the most significant information for providing contextualized mobile recommendation services at a certain time and place. However, it is very difficult for service providers to be aware of the personal contexts, because each person’s activities and preferences are very ambiguous and depending on numerous unknown factors. In order to deal with this problem, we have focused on discovering social relationships (e.g., family, friends, colleagues and so on) between people. We have assumed that the personal context of a certain person is interrelated with those of other people, and investigated how to employ his neighbor’s contexts, which possibly have a meaningful influence on his personal context. It indicates that we have to discover implicit social networks which express the contextual dependencies between people. Thereby, in this paper, we propose an interactive approach to build meaningful social networks by interacting with human experts. Given a certain social relation (e.g., isFatherOf), this proposed systems can evaluate a set of conditions (which are represented as propositional axioms) asserted from the human experts, and show them a social network resulted from data mining tools. More importantly, social network ontology has been exploited to consistently guide them by proving whether the conditions are logically verified, and to refine the discovered social networks. We expect these social network is applicable to generate context-based recommendation services. In this research project, we have applied the proposed system to discover the social networks between mobile users by collecting a dataset from about two millions of users.

Situational reasoning for task-oriented mobile service recommendation

AbstractWe study the case of integrating situational reasoning into a mobile service recommendation system. Since mobile Internet services are rapidly proliferating, finding and using appropriate services require profound service descriptions. As a consequence, for average mobile users it is nowadays virtually impossible to find the most appropriate service among the many offered. To overcome these difficulties, task navigation systems have been proposed to guide users towards best-fitting services. Our goal is to improve the user experience of such task navigation systems making them context-aware (i.e. to optimize service navigation by taking the user's situation into account). We propose the integration of a situational reasoning engine that applies classification-based inference to qualitative context elements, gathered from multiple sources and represented using ontologies. The extended task navigator enables the delivery of situation-aware recommendations in a proactive way. Initial experiments with the extended system indicate a considerable improvement of the navigator's usability.