User Ranking Research Articles

Study on the Psychological and Behavioral Patterns of College Students under the New Social Media

It is not only a theoretical problem, but also a practical problem to study the behavior of college students in social networks and the countermeasures of Ideological and political education. At present, the development of Internet and mobile communication technology for the birth of a new social media has become a contains a variety of social and cultural dissemination and communication, social psychology, group social groups living lifestyle and interpersonal relationships complex semantics of new media and social ecological network and information communication become more real-time, intuitive, convenient and widely, which also makes the subject consciousness and constantly enhance the contemporary college students, from the way of thinking to value orientation, from the aesthetic taste to the life etiquette is becoming more and more personalized and diversified, the choice of space is more broad. All these bring new opportunities and challenges to the ideological and political education in colleges and universities. With the development of the Internet social network and the popularity of smart phones, as young students more likely to try and accept new things, more and more college students to join the ranks of social networking users. In Microblogs, Webchat and other new media network platform to flourish, these new social media in the information at the same time, but also become an important platform for college students to express their own ideas and exchanges. In the "Several opinions of the Ministry of education to strengthen the ideological and political education work into the network" has made it clear that "according to changes in the environment and object of education, make full use of network means to expand the vision of Ideological and political education, correct, positive and healthy ideology and culture occupy the position of network". As a new carrier of College Students' network activity, social network has an important impact on college students. It is worth to pay attention to the ideological and political education work.

A novel rating prediction method based on user relationship and natural noise

Rating prediction is a hot spot in the research of recommender systems. There are lots of methods in this field such as collaborative filtering. However, few of these approaches take users’ friendship relationships into consideration, which actually contain significant information for rating prediction. Besides, there exists natural noise in users’ ratings. In this paper, we propose a rating prediction algorithm named NF-SVM based on the analysis of users’ natural noise and relationships. We cluster users to sharpen the similarity attribute among users, and use an iterative algorithm to obtain the rank of users’ rating quality. Then, we analyze users’ rating history to obtain the attributes of users’ natural noise. All these attributes are used to build a training set for SVM to get a prediction model. We also tested our algorithm in a data set which is crawled down from Douban, one of the largest movie rating web sites in China. Then we compared our algorithm with other state-of-the-art rating prediction methods. Extensive experiments show that our algorithm outperforms the other algorithms.

Hospital information system user acceptance factors: User group perspectives

ABSTRACTThis study aimed to identify and rank user acceptance factors regarding the implementation of hospital information systems (HIS) based on the views of the following user groups: doctors, nurses, hospital management, and administrative staff (operators). The results should provide guidance for hospital management and HIS developers on meeting user requirements. The study was carried out using both qualitative and quantitative methods. The authors conducted interviews and distributed questionnaires to doctors, nurses, hospital management, and administrative staff in a government-owned Indonesian public hospital. Entropy measurements were used to analyze the questionnaire data. The study identified 15 important HIS user acceptance factors, which were ranked differently by each user group. The results show that non-technological dimensions, such as human and organizational dimensions, influence HIS user acceptance to a greater extent than technological dimensions. More work should be carried out to gain a better understanding of the relationship between user acceptance factors in order to increase the success of HIS implementations.

A new ontology ranking method with OntoDUIA for ontology retrieval system

Ontology is being widely used for data integration and knowledge discovery in the field of data engineering. Ontology ranking is one of the important functions of ontology search engine, which ranks the searched ontologies in a reasonable order. A good ranking method can help a user acquiring the satisfied ontology efficiently from a considerable amount of search results. However, existing methods in literatures are unable to rank ontologies to well meet user's ranking demand due to their inherent shortcomings. In this paper, a novel ontology ranking method OntoDUIA is proposed and presented. It evaluates user-query related ontologies and ranks them based on a set of metrics of query relevance, usability, instance distribution and authority of ontology. To evaluate OntoDUIA, a series of experiments are conducted to compare the performance among OntoDUIA and some existing ontology ranking methods as well as human experts. Experimental results show that OntoDUIA can effectively meet user's ontology ranking demand, and it achieves stable and reliable ranking results. Finally, OntoDUIA can also be applied to ontology retrieval system.

Re-Ranking In User-Driven Reputation Systems With Splay Tree

In e-learning environments, trust relationship plays a vital role in establishing collaborative activities among co-learners. Reputation is a fundamental source for measuring co-learners' trust. An efficient and effective way to construct trust relationship among faculties and students in e-learning environment is ranking. User-driven reputation systems are based only on the feedback or ratings provided by the users. Users with higher points obtain high rating compared to less scored users. Thus, by Zipf's law, alleged low users are pushed to the bottom of the ranking list. This condition is avoided by encouraging less likely users and preventing them from moving further down in ranking level. Low ranked users are provided with few more chances to participate actively in the e-learning environment. A splay tree is a binary search tree with self-balancing skill. The splay tree brings the recently accessed item to the top of the tree. A splay tree is used to represent user's ranks, and low ranked users are semi-splayed again in the tree thus preventing them from further drowning in the ranking list for the fixed number of times. In this paper, we presented Rank_Improvement algorithm to enhance average scored users and compared with the ranking algorithm in the existing question-answering websites.

Cтраты участников локальных интернет-сообществ: основные черты и характеристики

This article discusses aspects of the stratification of network societies, in particular, describes the features of the existence of lines of stratification in the local Internet community. The study of the manifestations of various aspects of stratification in virtual groups based on a practical case study, which aims to become a social-stratification analysis of local online communities that exist in cyberspace large Russian cities. The summary of theoretical positions and the processing of the data, the author concludes that in any local community is there a stratification scale on which realized a ranking of users. A variety of criteria of the whole system status and role of the bundle, the skills of participants on the application of these criteria, the community influence the degree of use of formal and informal leaders of the community network moderation rights, and contribute ultimately to control interactions in the virtual group. Stratification in the local virtual teams is a system-nature, as in the absence of its social order is impossible. Meanwhile, the stable operation of the Internet community is not possible without the presence of the stratification system as a form of institutionalization of the social action. Abstracts of articles may be used in the implementation of socio-philosophical and sociological analysis of the phenomena caused by the development of the Internet

Detailed Investigation: Stratification of Phishing Websites Assisted by User Ranking Mechanism

Phishing is defined as developing a fake website similar as original to obtain users private information such as username, passwords and social security numbers. Now a days more number of research has been developed to identify fake websites but it has been done by original website owners based on user’s reviews. However still hacking users’ personal information’s is in active. Hence in this research we have proposed a new approach to identify fake websites from a huge number of websites that are available now is done by analyzing user’s reviews as primary factor and url identification. In url identification set of parameters and characteristics has been verified to identify whether uploaded new website has met our required needs if not then it considered to be phishing website. Second step is by keeping particular threshold value fake websites has been identified efficiently based on user’s reviews. The threshold value is designed as if same website obtained more number of bad reviews then it consider to be spam website. By implementing this two-step verification more number of phishing websites has been identified efficiently compared to existing approaches.

Context-aware Service Input Ranking by Learning from Historical Information

Users visit on-line services and compose them to accomplish on-line tasks, such as shopping on-line. Quite often, users enter the same information into various on-line services to finish on-line tasks. However, repetitively typing the same information into web forms is a tedious job for users. In this paper, we propose a context-aware ranking framework to rank values for input parameters. We propose 6 categories of ranking features constructed from various types of information, such as user contexts and patterns of user inputs. Our framework adopts learning-to-rank (LtR) algorithms that consist of a set of machine learned models to automatically construct ranking models by integrating the ranking features. When a user enters a value to an input parameter, an interaction between the user input and the input parameter is established. Our framework learns information relevant to such interactions and ranks user inputs in different contexts. Through empirical studies on the real-world on-line services, we obtain the following main results: (1) Among the 8 state-of-the-art learning-to-rank models, RankBoost can outperform other LtR models on ranking user inputs for input parameters; (2) Our framework using IRSVM that performs the worst among the LtR models outperforms the two baseline conventional ranking models and Google Chrome Autofilling, an industrial tool, on ranking user inputs to input parameters; and (3) We observe that the textual information of user inputs and input parameters is the most influential factor on ranking user inputs. Among the various types of contextual data, user locations and time matter the most to the ranking of user inputs.

Self‐organising multiuser matching in cellular networks: a score‐based mutually beneficial approach

In this study, the authors study the self-organising user assignment problem for the multi-user cooperation network. In the cellular network with user cooperation, idle users near the base station are seen as potential relay nodes, which can assist cell edge users to transmit data. Practically, the selfish nature of users is considered in the authors’ model. They design a score-based system, where the strategies of relay assignment are affected by the historical behaviours of users. That means not only the transmission performance, but also the accumulative contributions of users are considered. The proposed score-based system sufficiently encourages idle users to assist active users. Furthermore, the multi-user assignment problem is formulated as a one-to-one matching game, in which idle users and active users rank one another individually based on their own preference. To address the issue, they propose a self-organising mutually beneficial matching algorithm, which is proven to converge to a stable matching. Simulation results show that the proposed distributed algorithm yields well matching performance between source users and relay users, which is close to the optimal centralised results.

Social Content Recommendation Based on Spatial-Temporal Aware Diffusion Modeling in Social Networks

User interactions in online social networks (OSNs) enable the spread of information and enhance the information dissemination process, but at the same time they exacerbate the information overload problem. In this paper, we propose a social content recommendation method based on spatial-temporal aware controlled information diffusion modeling in OSNs. Users interact more frequently when they are close to each other geographically, have similar behaviors, and fall into similar demographic categories. Considering these facts, we propose multicriteria-based social ties relationship and temporal-aware probabilistic information diffusion modeling for controlled information spread maximization in OSNs. The proposed social ties relationship modeling takes into account user spatial information, content trust, opinion similarity, and demographics. We suggest a ranking algorithm that considers the user ties strength with friends and friends-of-friends to rank users in OSNs and select highly influential injection nodes. These nodes are able to improve social content recommendations, minimize information diffusion time, and maximize information spread. Furthermore, the proposed temporal-aware probabilistic diffusion process categorizes the nodes and diffuses the recommended content to only those users who are highly influential and can enhance information dissemination. The experimental results show the effectiveness of the proposed scheme.

Framework for Ranking of Web Forum Users

Web is used as a tool for presenting ideas, information and knowledge. Various users are a part of this web, if they be simple uses, influential users or non-influential users. There are a number of users on the web but a few users amongst these incorporate a relationship of faith and trust with the other users on the web. These users have a unique way in their activities and the quality of their comments and in this way they attract the other users on the web. Due to this uniqueness these users receive a special and significant attention amongst the many other users on the web. Such users play a dominating role in the community. Best example of this is a Blog, where an influential user leads and the others follow. By identifying such influential users we can measure the impact they have on the other users, the community, the neighbourhood as a whole. This paper presents a survey of various tools, algorithms, methods used and the different techniques applied to identify and rank the web forum users. Basically, Page Rank algorithm is used which gives a better and quick result followed by other techniques like User Rank and greedy algorithms. Keywords- web mining, influential users, blogs, PageRank

Resilience of an online financial community to market uncertainty shocks during the recent financial crisis

This paper examines the impact of market uncertainty shocks on an online forum of investors by measuring knowledge dynamics and user behaviour before and during the recent financial crisis. We looked at about 11 million messages of the finanzaonline.com online forum from 2003 to 2012 and measured user activity, the informative content of the posts and peer-to-peer communication. We also developed a ranking of forum users to measure their level of expertise. We found that market uncertainty has a dramatic effect on user population dynamics and the type of knowledge shared and that this depended on crisis evolution. While less expert user behaviour changed during the crisis evolution becoming more sensitive to external shocks, expert users typically pondered their response to shocks both before and during the crisis. Thanks to high variety of population dynamics, the forum played different roles depending on the stages of the crisis: sharing news during crisis outbreak, sharing more technical analysis during crisis progression. Our findings indicate the importance of promoting dynamism and communication opportunities in these online communities to give dispersed, non-professional investors the opportunity to collaborate in knowledge sharing and generation.

A Data-Based Approach to Discovering Multi-Topic Influential Leaders.

Recently, increasing numbers of users have adopted microblogging services as their main information source. However, most of them find themselves drowning in the millions of posts produced by other users every day. To cope with this, identifying a set of the most influential people is paramount. Moreover, finding a set of related influential users to expand the coverage of one particular topic is required in real world scenarios. Most of the existing algorithms in this area focus on topology-related methods such as PageRank. These methods mine link structures to find the expected influential rank of users. However, because they ignore the interaction data, these methods turn out to be less effective in social networks. In reality, a variety of topics exist within the information diffusing through the network. Because they have different interests, users play different roles in the diffusion of information related to different topics. As a result, distinguishing influential leaders according to different topics is also worthy of research. In this paper, we propose a multi-topic influence diffusion model (MTID) based on traces acquired from historic information. We decompose the influential scores of users into two parts: the direct influence determined by information propagation along the link structure and indirect influence that extends beyond the restrictions of direct follower relationships. To model the network from a multi-topical viewpoint, we introduce topic pools, each of which represents a particular topic information source. Then, we extract the topic distributions from the traces of tweets, determining the influence propagation probability and content generation probability. In the network, we adopt multiple ground nodes representing topic pools to connect every user through bidirectional links. Based on this multi-topical view of the network, we further introduce the topic-dependent rank (TD-Rank) algorithm to identify the multi-topic influential users. Our algorithm not only effectively overcomes the shortages of PageRank but also effectively produces a measure of topic-related rank. Extensive experiments on a Weibo dataset show that our model is both effective and robust.

Íntegro: Leveraging victim prediction for robust fake account detection in large scale OSNs

Detecting fake accounts in online social networks (OSNs) protects both OSN operators and their users from various malicious activities. Most detection mechanisms attempt to classify user accounts as real (i.e., benign, honest) or fake (i.e., malicious, Sybil) by analyzing either user-level activities or graph-level structures. These mechanisms, however, are not robust against adversarial attacks in which fake accounts cloak their operation with patterns resembling real user behavior.In this article, we show that victims – real accounts whose users have accepted friend requests sent by fakes – form a distinct classification category that is useful for designing robust detection mechanisms. In particular, we present Íntegro – a robust and scalable defense system that leverages victim classification to rank most real accounts higher than fakes, so that OSN operators can take actions against low-ranking fake accounts. Íntegro starts by identifying potential victims from user-level activities using supervised machine learning. After that, it annotates the graph by assigning lower weights to edges incident to potential victims. Finally, Íntegro ranks user accounts based on the landing probability of a short random walk that starts from a known real account. As this walk is unlikely to traverse low-weight edges in a few steps and land on fakes, Íntegro achieves the desired ranking.We implemented Íntegro using widely-used, open-source distributed computing platforms, where it scaled nearly linearly. We evaluated Íntegro against SybilRank, which is the state-of-the-art in fake account detection, using real-world datasets and a large-scale deployment at Tuenti – the largest OSN in Spain with more than 15 million active users. We show that Íntegro significantly outperforms SybilRank in user ranking quality, with the only requirement that the employed victim classifier is better than random. Moreover, the deployment of Íntegro at Tuenti resulted in up to an order of magnitude higher precision in fake account detection, as compared to SybilRank.

Collaborative Filtering Recommendation on Users' Interest Sequences.

As an important factor for improving recommendations, time information has been introduced to model users’ dynamic preferences in many papers. However, the sequence of users’ behaviour is rarely studied in recommender systems. Due to the users’ unique behavior evolution patterns and personalized interest transitions among items, users’ similarity in sequential dimension should be introduced to further distinguish users’ preferences and interests. In this paper, we propose a new collaborative filtering recommendation method based on users’ interest sequences (IS) that rank users’ ratings or other online behaviors according to the timestamps when they occurred. This method extracts the semantics hidden in the interest sequences by the length of users’ longest common sub-IS (LCSIS) and the count of users’ total common sub-IS (ACSIS). Then, these semantics are utilized to obtain users’ IS-based similarities and, further, to refine the similarities acquired from traditional collaborative filtering approaches. With these updated similarities, transition characteristics and dynamic evolution patterns of users’ preferences are considered. Our new proposed method was compared with state-of-the-art time-aware collaborative filtering algorithms on datasets MovieLens, Flixster and Ciao. The experimental results validate that the proposed recommendation method is effective and outperforms several existing algorithms in the accuracy of rating prediction.

Dynamics of Users Activity on Web-Blogs

Activity of users on Internet discussion forums is analyzed. The rank of users is shown to be approximated better by stretched-exponential function than by Zipfs law. Cumulative distribution function is found as an excellent tool in analysis of the dynamics of the collective social phenomena. We are able to approximate the number of blog comments with time by simple functions that resemble FermiDirac distribution function: probability of posting a comment is given by P(t) = P0(t)/(1+P0(t), where P0(t) = exp(a·log(t/t0)). It is argued that dynamics of blog entries by a specific user ought to be related to personality of each user.

Identification of Key Nodes in Microblog Networks

Microblog belongs to a new service of online social networks. For the perspective of information dissemination, the concept of spreading matrix is defined . A new WeiboR ank algorithm for identification of key nodes in the microblog networks is proposed taking into account parameters which includes the user direct appeal, the scope, and average information load. I n order to investigate how different measures correlate, we compare the relative influence rank of Top20 microblog users. The proposed algorithm is also compared with PageRank, Betweeness Centrality, Closeness Centrality, Out -degree algorithm using a new tweets propagation model ISR (Ignorants-Spreaders-Rejecters). Comparison results show that key nodes obtained from the WeiboRank algorithm have a wide r transmission range and better influence.

Health facilities humanisation: design guidelines supported by statistical evidence.

Healthcare building humanisation is currently a widely debated issue and the development of patient centered and evidence based design is growing worldwide. Many international health organizations and researchers understand the importance of Patient Centred Design and leading architects incorporate it into the design process. In Italy this design approach is still at an early stage. The article refers to research commissioned by the Italian Health Ministry and carried out by R. Del Nord (Università degli Studi di Firenze) and G. Peretti (Politecnico di Torino) with their collaborators. The scope of the research was the definition of design guidelines for healthcare facilities humanisation. The methodology framework adopted is the well established need and performance approach in architectural design. The article deals with the results of statistical investigations for the definition and ranking of users' needs and the consistent expression of their requirements. The investigations were carried out with the cooperation of psychologists of the Università degli Studi di Torino and researchers of the Università degli Studi di Cagliari. The proposed evaluation system allows ranking of health facilities according to the level of humanisation achieved. The statistical investigation evidence collected allowed the definition of humanisation design guidelines for health-care facilities and for the assessment of their specific level of humanisation.

A hybrid multi-criteria decision model for supporting customer-focused profitability analysis

Purpose – Strategic analysis of customer profitability for assessing market segmentation and reconfiguring customer relationship management (CRM) activities remains the key factor for achieving high return on CRM investment. The purpose of this paper is to map the profit-based ranking of corporate customers into the current market segments, with a view of determining the relative profitability of each market segment. Design/methodology/approach – This study develops a novel model that combines activity-based costing (ABC), CRM, fuzzy analytic hierarchy process (AHP), and technique for order preference by similarity to ideal solution (TOPSIS) methods to evaluate strategically customer profitability and prioritizing corporate accounts. This case study airline company has invested heavily in CRM over the past seven years on integrating multi-functional departments that touch customers. The airline operations management and marketing functions provide key inputs. Results of the hybrid model validate feasibility of the proposed model. Findings – The airline management makes use of the ranking results to optimize customer profitability by reconfiguring marketing programs, integrated schedule design, fleet assignment, maintenance routing, crew scheduling, and real-time optimization of schedule recovery in the aftermath of disruptions or irregularities. The proposed model also directs the marketing function to customize service offerings and introduce appropriate service levels to engage customers of different segments for the purpose of maximizing corporate profitability. Research limitations/implications – Significant amount of investment is necessary to design and implement the extensive CRM database and systems to assure customer data quality and availability so as to bear fruits in the proposed hybrid model. These data requirements can especially be a critical barrier for small to medium-sized companies. Practical implications – This hybrid model is able to capitalize on the benefits of the ABC, CRM, fuzzy AHP, and TOPSIS methods and offset their deficiencies. Most importantly, it can be applied to various industries without complex modification. Originality/value – This study represents the first move to adopt the fuzzy AHP and TOPSIS methods to analyze the ABC and CRM data inputs of an airline company. In mapping the profit-based ranking of corporate customers into the current market segments, the relative profitability of each market segment can be determined.

Testing of Junctional Tourniquets by Medics of the Israeli Defense Force in Control of Simulated Groin Hemorrhage

Junctional hemorrhage is a common cause of battlefield death but little is known about testing of junctional tourniquet models by medics. The purpose of the testing described herein is to assess military experience in junctional tourniquet use in simulated prehospital care.Fourteen medics were to use the following four junctional tourniquets: Combat Ready Clamp (CRoC), Abdominal Aortic Junctional Tourniquet (AAJT), Junctional Emergency Treatment Tool (JETT), and SAM Junctional Tourniquet (SJT). The five assessment categories were safety, effectiveness, time to effectiveness, and two categories of user preference: (1) by all models assessed, and (2) by only the model most preferred. Users ranked preference by answering, "If you had to go to war today and you could only choose one, which tourniquet would you choose to bring?"All tourniquet uses were safe. By the time the first five testers were done, all three AAJT models had been broken. CRoC and AAJT had the highest percentage effectiveness as their difference was not statistically significant. SJT and JETT had fastest mean times to effectiveness as their difference was not significant. For preference, using each user's ranking of all models assessed, SJT and AAJT were most preferred as their difference was not significant. For each user's most preferred model, SJT, AAJT, and JETT were most preferred as their difference was not significant.In the five assessment categories, multiple tourniquet models performed similarly well; SJT and AAJT performed best in four categories, JETT was best in three, and CRoC was best in two. Differences between the top-ranked models in each category were not statistically significant.