User Preference Aware Hierarchical Edge-User Cooperative Caching Strategy

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.

Deep Learning Social Network Access Control Model Based on User Preferences

A deep learning access controlmodel based on user preferences is proposed to address the issue of personal privacy leakage in social networks.Firstly,socialusers andsocialdata entities are extractedfromthe social networkandused to construct homogeneous and heterogeneous graphs.Secondly,a graph neural networkmodel is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social network.Then,high-order neighbor nodes,hidden neighbor nodes,displayed neighbor nodes,and social data nodes are used to update user nodes to expand the depth and breadth of user preferences.Finally,a multi-layer attention network is used to classify user nodes in the homogeneous graph into two classes:allow access and deny access.The fine-grained access control problem in social networks is transformed into a node classification problem in a graph neural network.The model is validated using a dataset and compared with other methods without losing generality.The model improved accuracy by 2.18%compared to the baseline method GraphSAGE,and improved F1 score by 1.45%compared to the baseline method,verifying the effectiveness of the model.

Personalized web pages ranking algorithm based on user preferences

In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web pages in accordance with user preferences is proposed.PWPR assigns the initial weights based on user interests and creates the virtual links and hubs according to user interests.By measuring user click streams,PWPR incrementally reflects users’ favors for the personalized ranking.To improve the accuracy of ranking, PWPR also takes collaborative filtering into consideration when the query with similar is submitted by users who have similar user interests. Detailed simulation results and comparison with other algorithms prove that the proposed PWPR can adaptively provide personalized ranking and truly relevant information to user preferences.

User Preference Survey on Staircase Rotation

Staircase is an important means of vertical transportation. Staircase design exerts a great influence on the aesthetics, transportation efficiency, user comfort and experience level. In this paper, a survey on the staircase rotation preference was conducted, based on the environment behavior studies. Different user frequencies of a pair of scissors stairs in the 2nd teaching building of North China University of Technology were analyzed. The psychological effect was evaluated and quantified, and the user preference on the two staircase rotations was then withdrawn. The survey found that the type of staircase with clockwise upstairs was much more preferred （78%） than the other staircase rotation anti-clock upstairs. Considering different genders, the female shows a 66% higher preference inclination of this type of staircase rotation than the male. To improve the transportation efficiency of the staircase in case of fire, the result of this paper can be very constructive for the evacuation staircase rotation choice for the high-rise buildings.

Research on Influencing Factors of Information Dissemination Based on User Preference

The rapid development of the Intemet makes the social network of information dissemination has undergone tremendous changes. Based on the introduction of social network information dissemination mode, this paper analyzes the influencing factors of information dissemination, establishes the user preference model through CP-nets tool, and combines the AHP principle to mine the user＇s preference order, and obtain the user＇s optimal preference feature Portfolio, and finally collect the user in the microblogging platform in the historical behavior data. the use of NetLogo different users of information dissemination decision to predict.

Effects of smartphone icon background shapes and figure/background area ratios on visual search performance and user preferences

Smartphones are becoming increasingly popular, users are provided with various interface styles with different designed icons. Icon, as an important competent of user interface, is regarded to be more efficient and pleasurable. However, compared with desktop computers, fewer design principles on smartphone icon were proposed. This paper investigated the effects of icon background shape and the figure/background area ratio on visual search performance and user preference. Icon figures combined with six different geometric background shapes and five different figure/ background area ratios were studied on three different screens in experiments with 40 subjects. The results of an analysis of variance （ANOVA） showed that these two inde- pendent variables （background shape and figure/background area ratio） significantly affected the visual search performance and user preference. On 3.5-in （1 in=0.025 4 m） and 4.0-in displays, unified backgroundwould be optimal, shapes such as square, circle and transitions between them （e.g., rounded square, squircle, etc.） are recommended because backgrounds in these shapes yield a better search time performance and subjective satisfaction for ease of use, search and visual preference. A 60% figure/background area ratio is the most appropriate for smartphone icon design on the 3.5-in screen, while a 50% area ratio could be a suggestion for both relatively optimized search performance and user preference on 4.0-in. In terms of the 4.7-in, icon figure is used di- rectly for its better performance and preference compared with icons with background.

A strategy-proof auction mechanism for service composition based on user preferences

Service composition is an effective method of combining existing atomic services into a value-added service based on cost and quality of service(QoS).To meet the diverse needs of users and to offer pricing services based on QoS,we propose a service composition auction mechanism based on user preferences,which is strategy-proof and can be beneficial in selecting services based on user preferences and dynamically determining the price of services.We have proven that the proposed auction mechanism achieves desirable properties including truthfulness and individual rationality.Furthermore,we propose an auction algorithm to implement the auction mechanism,and carry out extensive experiments based on real data.The results verify that the proposed auction mechanism not only achieves desirable properties,but also helps users find a satisfactory service composition scheme.

Incorporating User’s Preferences into Scholarly Publications Recommendation

Over the years, there has been increasing growth in academic digital libraries. It has therefore become overwhelming for researchers to determine important research materials. In most existing research works that consider scholarly paper recommendation, the researcher’s preference is left out. In this paper, therefore, Frequent Pattern (FP) Growth Algorithm is employed on potential papers generated from the researcher’s preferences to create a list of ranked papers based on citation features. The purpose is to provide a recommender system that is user oriented. A walk through algorithm is implemented to generate all possible frequent patterns from the FP-tree after which an output of ordered recommended papers combining subjective and objective factors of the researchers is produced. Experimental results with a scholarly paper recommendation dataset show that the proposed method is very promising, as it outperforms recommendation baselines as measured with nDCG and MRR.

Ontology-based framework for personalized recommendation in digital libraries

To promote information service ability of digital libraries, a browsing and searching personalized recommendation framework based on the use of ontology is described, where the advantages of ontology are exploited in different parts of the retrieval cycle including query-based relevance measures, semantic user preference representation and automatic update, and personalized result ranking. Both the usage and information resources can be exploited to extract useful knowledge from the way users interact with a digital library. Through combination and mapping between the extracted knowledge and domain ontology, semantic content retrieval between queries and documents can be utilized. Furthermore, ontology-based conceptual vector of user preference can be applied in personalized recommendation feedback.

QoS Evaluation for Web Service Recommendation

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.

A study on service quality evaluation of digital libraries in China based on tetra-class model

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.

HilAnchor:Location Privacy Protection in the Presence of Users' Preferences

Location privacy receives considerable attentions in emerging location based services.Most current practices however either ignore users＇ preferences or incompletely fulfill privacy preferences.In this paper,we propose a privacy protection solution to allow users＇ preferences in the fundamental query of k nearest neighbors （kNN）.Particularly,users are permitted to choose privacy preferences by specifying minimum inferred region.Via Hilbert curve based transformation,the additional workload from users＇ preferences is alleviated.Furthermore,this transformation reduces time-expensive region queries in 2-D space to range the ones in 1-D space.Therefore,the time efficiency,as well as communication efficiency,is greatly improved due to clustering properties of Hilbert curve.Further,details of choosing anchor points are theoretically elaborated.The empirical studies demonstrate that our implementation delivers both flexibility for users＇ preferences and scalability for time and communication costs.

Mining Interesting Knowledge from Web-Log

Web-log contains a lot of information related with user activities on the Internet. How to mine user browsing interest patterns effectively is an important and challengeable research topic. On the analysis of the present algorithm’s advantages and disadvantages we propose a new concept: support-interest. Its key insight is that visitor will backtrack if they do not find the information where they expect. And the point from where they backtrack is the expected location for the page. We present User Access Matrix and the corresponding algorithm for discovering such expected locations that can handle page caching by the browser. Since the URL-URL matrix is a sparse matrix which can be represented by List of 3-tuples, we can mine user preferred sub-paths from the computation of this matrix. Accordingly, all the sub-paths are merged, and user preferred paths are formed. Experiments showed that it was accurate and scalable. It’s suitable for website based application, such as to optimize website’s topological structure or to design personalized services. Key words Web Mining - user preferred path - Web-log - support-interest - personalized services CLC number TP 391 Foundation item: Supported by the National High Technology Development (863 program of China) (2001AA113182)Biography: ZHOU Hong-fang (1976-), female.Ph. D candidate, research direction: data mining and knowledge discovery in databases.

Design and Realization of an NFC-Driven Smart Home System to Support Intruder Detection and Social Network Integration

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.

Building a better recommender system in E-commerce

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.

Location-Aware Personalized Traveler Recommender System(LAPTA)Using Collaborative Filtering KNN

Many tourists who travel to explore different cultures and cities worldwide aim to find the best tourist sites,accommodation,and food according to their interests.This objective makes it harder for tourists to decide and plan where to go and what to do.Aside from hiring a local guide,an option which is beyond most travelers’budgets,the majority of sojourners nowadays use mobile devices to search for or recommend interesting sites on the basis of user reviews.Therefore,this work utilizes the prevalent recommender systems and mobile app technologies to overcome this issue.Accordingly,this study proposes location-aware personalized traveler assistance(LAPTA),a system which integrates user preferences and the global positioning system(GPS)to generate personalized and location-aware recommendations.That integration will enable the enhanced recommendation of the developed scheme relative to those from the traditional recommender systems used in customer ratings.Specifically,LAPTA separates the data obtained from Google locations into name and category tags.After the data separation,the system fetches the keywords from the user’s input according to the user’s past research behavior.The proposed system uses the K-Nearest algorithm to match the name and category tags with the user’s input to generate personalized suggestions.The system also provides suggestions on the basis of nearby popular attractions using the Google point of interest feature to enhance system usability.The experimental results showed that LAPTA could provide more reliable and accurate recommendations compared to the reviewed recommendation applications.

Personalized Recommendation Algorithm Based on Preference Features

A hybrid collaborative filtering algorithm based on the user preferences and item features is proposed.A thorough investigation of Collaborative Filtering （CF） techniques preceded the development of this algorithm.The proposed algorithm improved the user-item similarity approach by extracting the item feature and applying various item features＇ weight to the item to confirm different item features.User preferences for different item features were obtained by employing user evaluations of the items.It is expected that providing better recommendations according to preferences and features would improve the accuracy and efficiency of recommendations and also make it easier to deal with the data sparsity.In addition,it is expected that the potential semantics of the user evaluation model would be revealed.This would explain the recommendation results and increase accuracy.A portion of the MovieLens database was used to conduct a comparative experiment among the proposed algorithms,i.e.,the collaborative filtering algorithm based on the item and the collaborative filtering algorithm based on the item feature.The Mean Absolute Error （MAE） was utilized to conduct performance testing.The experimental results show that employing the proposed personalized recommendation algorithm based on the preference-feature would significantly improve the accuracy of evaluation predictions compared to two previous approaches.

A multi-preference integrated algorithm(MPIA)for the deep learning-based recommender framework(DLRF)

Purpose-The deep learning-based recommender framework(DLRF)is based on an improved long short-term memory(LSTM)structurewith additional controllers;thus,it considers contextual information for state transition.It also handles irregularities in the data to enhance performance in generating recommendations while modelling short-term preferences.An algorithm named a multi-preference integrated algorithm(MPIA)is proposed to have dynamic integration of both kinds of user preferences aforementioned.Extensive experiments are made using Amazon benchmark datasets,and the results are compared with many existing recommender systems(RSs).Design/methodology/approach-RSs produce quality information filtering to the users based on their preferences.In the contemporary era,online RSs-based collaborative filtering(CF)techniques are widely used to model long-term preferences of users.With deep learning models,such as recurrent neural networks(RNNs),it became viable to model short-term preferences of users.In the existing RSs,there is a lack of dynamic integration of both long-and short-term preferences.In this paper,the authors proposed a DLRF for improving the state of the art in modelling short-term preferences and generating recommendations as well.Findings-The results of the empirical study revealed that the MPIA outperforms existing algorithms in terms of performance measured using metrics such as area under the curve(AUC)and F1-score.The percentage of improvement in terms AUC is observed as 1.3,2.8,3 and 1.9%and in terms of F-1 score 0.98,2.91,2 and 2.01%on the datasets.Originality/value-The algorithm uses attention-based approaches to integrate the preferences by incorporating contextual information.

Estimation of charging demand for electric vehicles by discrete choice models and numerical simulations: Application to a case study in Turin

The electrification of vehicles is considered one of the most important strategies for addressing the issues related to energy dependence and climate change.To meet user needs,electric vehicle(EV)management for charging operations is essential.This study uses modelling and simulation of EV user behaviour to forecast possible scenarios for electric charging in cities and to identify potential management problems and opportunities for improvement of EVs and EV charging infrastructures.The conurbation of Turin was selected as a case study to reproduce realistic scenarios by applying discrete choice modelling based on socio-economic and transport system data.One of objectives of the study was to describe user charging behaviour from a geographic perspective to model where users prefer to charge in the area studied according to the variables that may affect decisions.Another objective was to estimate the number of electric vehicles in Turin and the characteristics of their users,both of which are helpful in understanding electric mobility within a city.Analysing these behavioural issues in a modelling framework can provide a set of tools to compare and evaluate a variety of possible modifications,indicating an adequate network of charging infrastructure to facilitate the diffusion of electric vehicles.

Personalized Service System Based on Hybrid Filtering for Digital Library

Personalized service systems are an effective way to help users obtain recommendations for unseen items, within the enormous volume of information available based on their preferences. The most commonly used personalized service system methods are collaborative filtering, content-based filtering, and hybrid filtering. Unfortunately, each method has its drawbacks. This paper proposes a new method which unified partition-based collaborative filtering and meta-information filtering. In partition-based collaborative filtering the user-item rating matrix can be partitioned into low-dimensional dense matrices using a matrix clustering algorithm. Recommendations are generated based on these low-dimensional matrices. Additionally, the very low ratings problem can be solved using meta-information filtering. The unified method is applied to a digital resource management system. The experimental results show the high efficiency and good performance of the new approach.