The emergence of various new services has posed a huge challenge to the existing network architecture.To improve the network delay and backhaul pressure,caching popular contents at the edge of network has been conside...The emergence of various new services has posed a huge challenge to the existing network architecture.To improve the network delay and backhaul pressure,caching popular contents at the edge of network has been considered as a feasible scheme.However,how to efficiently utilize the limited caching resources to cache diverse contents has been confirmed as a tough problem in the past decade.In this paper,considering the time-varying user requests and the heterogeneous content sizes,a user preference aware hierarchical cooperative caching strategy in edge-user caching architecture is proposed.We divide the caching strategy into three phases,that is,the content placement,the content delivery and the content update.In the content placement phase,a cooperative content placement algorithm for local content popularity is designed to cache contents proactively.In the content delivery phase,a cooperative delivery algorithm is proposed to deliver the cached contents.In the content update phase,a content update algorithm is proposed according to the popularity of the contents.Finally,the proposed caching strategy is validated using the MovieLens dataset,and the results reveal that the proposed strategy improves the delay performance by at least 35.3%compared with the other three benchmark strategies.展开更多
Web service recommendation is one of the most important fi elds of research in the area of service computing. The two core problems of Web service recommendation are the prediction of unknown Qo S property values and ...Web service recommendation is one of the most important fi elds of research in the area of service computing. The two core problems of Web service recommendation are the prediction of unknown Qo S property values and the evaluation of overall Qo S according to user preferences. Aiming to address these two problems and their current challenges, we propose two efficient approaches to solve these problems. First, unknown Qo S property values were predicted by modeling the high-dimensional Qo S data as tensors, by utilizing an important tensor operation, i.e., tensor composition, to predict these Qo S values. Our method, which considers all Qo S dimensions integrally and uniformly, allows us to predict multi-dimensional Qo S values accurately and easily. Second, the overall Qo S was evaluated by proposing an efficient user preference learning method, which learns user preferences based on users' ratings history data, allowing us to obtain user preferences quantifiably and accurately. By solving these two core problems, it became possible to compute a realistic value for the overall Qo S. The experimental results showed our proposed methods to be more efficient than existing methods.展开更多
Purpose: This study intends to evaluate the service quality of academic digital libraries(DLs)in China. By utilizing tetra-class model which concentrates on categorizing services according to their contributions to us...Purpose: This study intends to evaluate the service quality of academic digital libraries(DLs)in China. By utilizing tetra-class model which concentrates on categorizing services according to their contributions to user satisfaction, this paper attempts to visually categorize the specific DL service elements to reveal their present performances, and then further explain the categorizing variations among different groups of users to discover the user preference.Design/methodology/approach: This paper carries out a survey to evaluate user experience on 27 typical DL services summarized from our investigations of representative Chinese university DLs. Based on the five-point Likert-type scale evaluation, the users’ attitudes toward specific service element are divided into negative and positive dimensions. Afterwards,a correspondence analysis is applied to calculate the contributions to satisfaction and dissatisfaction of each service element based on tetra-class model. As a result, the DL service elements of Chinese academic libraries are classified into four categories(i.e. Basic, Secondary,Plus, and Key). Finally, we compared the categorizing variations.Findings: The results show that the DL service elements of Chinese academic libraries are all distributed in Basic and Key services regarding information retrieval and informationorganizing; 80% of the interaction services elements are Plus services, while 50% of the Secondary services are information-providing services. The results also reveal that service categorization is obviously influenced by the users’ education background, especially their disciplines. Furthermore, the users who are older, more highly-educated, or studying in higher reputation universities are more likely to evaluate DL services as either critical or useless.Research limitations: Tetra-class model cannot reveal the interplay among the DL service elements. In addition, the user segmentation in our studies is limited to the sample structure.Practical implications: This empirical study focuses on the evaluation of DL services of academic libraries in China, the analyses of their current performances could provide useful reference for the assessment of other types of Chinese DLs. Moreover, the consideration of user characteristics(gender, age, and education background, etc.) in the DL evaluation would help librarians improve DL services to meet the users’ various needs in teaching and doing scientific research.Originality/value: Different from the frequently-used factor analysis which focuses on the relationship among factors and user satisfaction, this paper tries to use and compare element distributions of different user segments while focusing on various service objectives. Factor analysis shows some flaws as used to measure the element with selected indicators, for it ignores the fact that the indicators which measure the same factor would have different degrees of impacts on user satisfaction. However, the tetra-class model can better visually analyze the performance of each DL service element from its contributions to satisfaction and dissatisfaction, which would help librarians to better understand users’ need and offer DL services more efficiently.展开更多
The Internet of thing（Io T） emerges as a possible solution to realize a smart life in the modern age. In this article, we design and realize a novel near field communication（NFC）-driven smart home system for Io T,...The Internet of thing（Io T） emerges as a possible solution to realize a smart life in the modern age. In this article, we design and realize a novel near field communication（NFC）-driven smart home system for Io T, which integrates the wireless sensor network（WSN）, social networks, and the cloud computing. NFC technology provides a way for users to exchange information between them and the system by simply contacting. So, we propose to use NFC as the system drive in the architecture, such that users can intuitively interact with the system and deliver their intentions. Then, the corresponding service over the system will control or adjust the “things” at home to fit users＇ needs. Furthermore, the proposed system provides a platform for developers to easily and rapidly implement their smart home related services. In the system, WSN sensing and control, NFC communications and identification, user profile management and preference analysis, and social network integration are all provided as platform services. We will show how the system works for home automation, intruder detection, and social network sharing.展开更多
This paper presents an architecture of a hybrid recommender system in E-commerce environment. The goal of the system is to make special improvements in giving precisely personalized recommendation through some effecti...This paper presents an architecture of a hybrid recommender system in E-commerce environment. The goal of the system is to make special improvements in giving precisely personalized recommendation through some effective measures. Based on the study on the existing recommendation methods of both the conventional similarity function and the conventional feedback function, several improvement algorithms are developed to enhance the precision of recommendation, which include three improved similarity functions, four improved feedback functions, and adoption of both explicit and implicit preferences in individual user profile. Among them, issues and countermeasures of a new user, prominent preferences and long-term preferences are nicely addressed to gain better recommendation. The users preferences is so designed to be precisely captured by a user-side agent, and can make self-adjustment with explicit or implicit feedback.展开更多
基金supported by Natural Science Foundation of China(Grant 61901070,61801065,62271096,61871062,U20A20157 and 62061007)in part by the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant KJQN202000603 and KJQN201900611)+3 种基金in part by the Natural Science Foundation of Chongqing(Grant CSTB2022NSCQMSX0468,cstc2020jcyjzdxmX0024 and cstc2021jcyjmsxmX0892)in part by University Innovation Research Group of Chongqing(Grant CxQT20017)in part by Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)in part by the Chongqing Graduate Student Scientific Research Innovation Project(CYB22246)。
文摘The emergence of various new services has posed a huge challenge to the existing network architecture.To improve the network delay and backhaul pressure,caching popular contents at the edge of network has been considered as a feasible scheme.However,how to efficiently utilize the limited caching resources to cache diverse contents has been confirmed as a tough problem in the past decade.In this paper,considering the time-varying user requests and the heterogeneous content sizes,a user preference aware hierarchical cooperative caching strategy in edge-user caching architecture is proposed.We divide the caching strategy into three phases,that is,the content placement,the content delivery and the content update.In the content placement phase,a cooperative content placement algorithm for local content popularity is designed to cache contents proactively.In the content delivery phase,a cooperative delivery algorithm is proposed to deliver the cached contents.In the content update phase,a content update algorithm is proposed according to the popularity of the contents.Finally,the proposed caching strategy is validated using the MovieLens dataset,and the results reveal that the proposed strategy improves the delay performance by at least 35.3%compared with the other three benchmark strategies.
基金supported by the Natural Science Foundation of Beijing under Grant No.4132048NSFC (61472047),and NSFC (61202435)
文摘Web service recommendation is one of the most important fi elds of research in the area of service computing. The two core problems of Web service recommendation are the prediction of unknown Qo S property values and the evaluation of overall Qo S according to user preferences. Aiming to address these two problems and their current challenges, we propose two efficient approaches to solve these problems. First, unknown Qo S property values were predicted by modeling the high-dimensional Qo S data as tensors, by utilizing an important tensor operation, i.e., tensor composition, to predict these Qo S values. Our method, which considers all Qo S dimensions integrally and uniformly, allows us to predict multi-dimensional Qo S values accurately and easily. Second, the overall Qo S was evaluated by proposing an efficient user preference learning method, which learns user preferences based on users' ratings history data, allowing us to obtain user preferences quantifiably and accurately. By solving these two core problems, it became possible to compute a realistic value for the overall Qo S. The experimental results showed our proposed methods to be more efficient than existing methods.
基金supported by the National Natural Science Foundation of China (Grant No.:71273197)
文摘Purpose: This study intends to evaluate the service quality of academic digital libraries(DLs)in China. By utilizing tetra-class model which concentrates on categorizing services according to their contributions to user satisfaction, this paper attempts to visually categorize the specific DL service elements to reveal their present performances, and then further explain the categorizing variations among different groups of users to discover the user preference.Design/methodology/approach: This paper carries out a survey to evaluate user experience on 27 typical DL services summarized from our investigations of representative Chinese university DLs. Based on the five-point Likert-type scale evaluation, the users’ attitudes toward specific service element are divided into negative and positive dimensions. Afterwards,a correspondence analysis is applied to calculate the contributions to satisfaction and dissatisfaction of each service element based on tetra-class model. As a result, the DL service elements of Chinese academic libraries are classified into four categories(i.e. Basic, Secondary,Plus, and Key). Finally, we compared the categorizing variations.Findings: The results show that the DL service elements of Chinese academic libraries are all distributed in Basic and Key services regarding information retrieval and informationorganizing; 80% of the interaction services elements are Plus services, while 50% of the Secondary services are information-providing services. The results also reveal that service categorization is obviously influenced by the users’ education background, especially their disciplines. Furthermore, the users who are older, more highly-educated, or studying in higher reputation universities are more likely to evaluate DL services as either critical or useless.Research limitations: Tetra-class model cannot reveal the interplay among the DL service elements. In addition, the user segmentation in our studies is limited to the sample structure.Practical implications: This empirical study focuses on the evaluation of DL services of academic libraries in China, the analyses of their current performances could provide useful reference for the assessment of other types of Chinese DLs. Moreover, the consideration of user characteristics(gender, age, and education background, etc.) in the DL evaluation would help librarians improve DL services to meet the users’ various needs in teaching and doing scientific research.Originality/value: Different from the frequently-used factor analysis which focuses on the relationship among factors and user satisfaction, this paper tries to use and compare element distributions of different user segments while focusing on various service objectives. Factor analysis shows some flaws as used to measure the element with selected indicators, for it ignores the fact that the indicators which measure the same factor would have different degrees of impacts on user satisfaction. However, the tetra-class model can better visually analyze the performance of each DL service element from its contributions to satisfaction and dissatisfaction, which would help librarians to better understand users’ need and offer DL services more efficiently.
基金supported in part by the NSC under Grant No.103-2815-C-024-013-E and 102-2218-E-009-014-MY3the MOST under Grant No.103-2221-E-024-005
文摘The Internet of thing（Io T） emerges as a possible solution to realize a smart life in the modern age. In this article, we design and realize a novel near field communication（NFC）-driven smart home system for Io T, which integrates the wireless sensor network（WSN）, social networks, and the cloud computing. NFC technology provides a way for users to exchange information between them and the system by simply contacting. So, we propose to use NFC as the system drive in the architecture, such that users can intuitively interact with the system and deliver their intentions. Then, the corresponding service over the system will control or adjust the “things” at home to fit users＇ needs. Furthermore, the proposed system provides a platform for developers to easily and rapidly implement their smart home related services. In the system, WSN sensing and control, NFC communications and identification, user profile management and preference analysis, and social network integration are all provided as platform services. We will show how the system works for home automation, intruder detection, and social network sharing.
文摘This paper presents an architecture of a hybrid recommender system in E-commerce environment. The goal of the system is to make special improvements in giving precisely personalized recommendation through some effective measures. Based on the study on the existing recommendation methods of both the conventional similarity function and the conventional feedback function, several improvement algorithms are developed to enhance the precision of recommendation, which include three improved similarity functions, four improved feedback functions, and adoption of both explicit and implicit preferences in individual user profile. Among them, issues and countermeasures of a new user, prominent preferences and long-term preferences are nicely addressed to gain better recommendation. The users preferences is so designed to be precisely captured by a user-side agent, and can make self-adjustment with explicit or implicit feedback.
