AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

Behavioral Intention to Continue Using a Library Mobile App

To meet the needs of today’s library users,institutions are developing library mobile apps(LMAs),as their libraries are increasingly intelligent and rely on deep learning.This paper explores the influencing factors and differences in the perception of LMAs at different time points after a user has downloaded an LMA.A research model was constructed based on the technology acceptance model.A questionnaire was designed and distributed twice to LMA users with an interval of three months to collect dynamic data.The analysis was based on structural equation modeling.The empirical results show that the perceived ease of use,the perceived usefulness,the social influence,and the facilitating conditions affected the users’behavioral intention,but their impacts were different at different times.As the usage time increases,the technology acceptance model is still universal for understanding the user perception of LMA.In addition,two extended variables(social impact and convenience)also affect the user’s behavior intention.User behavior is dynamic and changed over time.This study is important both theoretically and practically,as the results could be used to improve the service quality of LMAs and reduce the loss rate of users.Its findings may help the designers and developers of LMAs to optimize them from the perspective of a user and improve the service experience by providing a deeper understanding of the adoption behavior of information systems by LMA users.

Factors influencing academic library users' intention to use mobile systems: A comparison of current users and potential adopters

Purpose: The study intends to examine the factors influencing the behavioral intention to use academic libraries’ mobile systems from the perspective of current users and potential adopters, respectively. Design/methodology/approach: Our study investigates the mobile library system’s acceptance by using a context-specific extension of the theory of reasoned action(TRA) and the technology acceptance model(TAM), which includes such factors as mobile self-efficacy, personal innovativeness and perceived playfulness. Structural equation modeling was used to test the validity of the proposed model based on the empirical data which was collected from 210 questionnaire survey participants.Findings: The result shows that 1) for both current users and potential adopters, attitude toward use and subjective norm both have a significant and positive impact on behavioral intention to use; 2) perceived usefulness and perceived ease of use are significantly correlated to potential adopters’ attitude toward use whereas perceived usefulness and perceived playfulness are significantly related to current users’ attitude toward use; 3) as for the comparison between the two groups of users, personal innovativeness not only affects perceived usefulness of both current users and potential adopters, but also affects potential adopters’ perceived playfulness positively. Mobile self-efficacy has a significant effect on perceived ease of use for both types of users.Research limitations: Although the sample size met the basic statistics requirements for the social research, the participants were mainly college students, and other mobile system users like faculty members and researchers were not investigated. In addition, some influencing factors, such as information quality, system quality and service quality were not considered in the research model.Practical implications: This study reveals main factors which influence both current users and potential adopters’ intention to use the mobile system, providing academic libraries withinsights into management strategies to offer customized mobile services to different types of users. Originality/value: Previous studies did not distinguish current users from potential adopters, which is not conducive for academic libraries to provide customized services and attract potential users. We presented an exploratory study to address this issue.

Learning User Intention in Networked Mobile Robot Control

Teleoperated networked robot often has unpredictable behaviors due to uncertain time delay from data transmission over Internet. The robot cannot accomplish the desired actions of the remote operator in time, which severely impairs reliability and efficiency of the robot system. This paper investigated a novel approach, learning user intention, to compensate the uncertain time delay with the autonomy of a mobile robot. The user intention to control and operate the robot was modeled and incrementally inferred based on Bayesian techniques so that the desired actions could be recognized and completed by the robot autonomously. Thus the networked robot is able to fulfill the task assigned without frequent interaction with the user, which decreases data transmission and improves the efficiency of the whole system. Experimental results show the validity and feasibility of the proposed method.

A Joint Model of Device Load and User Intention in Load Monitoring

In this paper, a concept for the joint modeling of the device load and user intention is presented. It consists of two coupled models, a device load model to characterize the power consumption of an electric device of interest, and a user intention model for describing the user intentions which cause the energy consumption. The advantage of this joint model is the ability to predict the device load from the user intention and to reconstruct the user intention from the measured device load. This opens a new way for load monitoring, simulation and prediction from the perspective of users instead of devices.

TdBrnn:An Approach to Learning Users’Intention to Legal Consultation with Normalized Tensor Decomposition and Bi-LSTM

With the development of Internet technology and the enhancement of people’s concept of the rule of law,online legal consultation has become an important means for the general public to conduct legal consultation.However,different people have different language expressions and legal professional backgrounds.This phenomenon may lead to the phenomenon of different descriptions of the same legal consultation.How to accurately understand the true intentions behind different users’legal consulting statements is an important issue that needs to be solved urgently in the field of legal consulting services.Traditional intent understanding algorithms rely heavily on the lexical and semantic information between the original data,and are not scalable,and often require taxing manual annotation work.This article proposes a new approach TdBrnn which is based on the normalized tensor decomposition method and Bi-LSTM to learn users’intention to legal consulting.First,we present the users’legal consulting statements as a tensor.And then we use the normalized tensor decomposition layer proposed by this article to extract the tensor elements and structural information of the original tensor which can best represent users’intention of legal consultation,namely the core tensor.The core tensor relies less on the lexical and semantic information of the original users’legal consulting statements data,it reduces the dimension of the original tensor,and greatly reduces the computational complexity of the subsequent Bi-LSTM algorithm.Furthermore,we use a large number of core tensors obtained by the tensor decomposition layer with users’legal consulting statements tensors as inputs to continuously train Bi-LSTM,and finally derive the users’legal consultation intention classification model which can comprehensively understand the user’s legal consultation intention.Experiments show that our method has faster convergence speed and higher accuracy than traditional recurrent neural networks.

Reducing the Negative Impact of Pop-up Advertisements on Users and Improving Potential Purchase Intention

With the development of the internet,online pop-up advertisements(hereinafter,referred to as pop-up ads)have emerged.However,online users may disdain and reject online advertisements,which affects online purchase intention.This study is on reducing the negative impact of pop-up ads on users and improving the commercial effect.Self-administered questionnaires were used to survey online users and website designers.The data collected were analyzed using SPSS and the open answers were sorted out by thematic analysis.The results revealed that attractive storylines,background music,and exquisite visual presentations are effective in reducing users5 rejection to pop-up ads as well as advertisement customization.It is better for pop-up ads to appear in the middle or end of videos.The VIP system is also a choice for users to eliminate them.Designers are supposed to keep a balance between users and advertisers.In addition,internet regulation needs to be strengthened to reduce eroticism and violence in pop-up ads as well as avoid the negative impact of these kind of pop-up ads on minors.

基于S-O-R模型的视觉营销对消费者购买意愿的影响研究——以红色文创网店为例

为提高红色文创网店的点击转化率,文章从消费者视角出发,研究影响消费者购买意愿的视觉因素。基于S-O-R模型理论,构建红色文创网店视觉营销对消费者购买意愿因素的理论模型。利用S-O-R模型分析了红色文创网店视觉营销中的商品形象、界面形象以及品牌视觉形象对顾客感知价值及顾客购买意向的影响。通过设计量表和调查问卷进行实证研究,并结合结构方程模型加以论证,得出品牌视觉形象和界面形象是影响消费者购买意愿的关键因素,为设计师和红色文创品牌进行网店设计提供了一定的理论参考和建议。

An empirical study on continuance intention of mobile reading

Purpose： This study aims to explore factors affecting continuance intention of mobile reading. Design/methodology/approach： Drawing on the unified theory of acceptance and use of technology （UTAUT）, and integrating perceived enjoyment, we put forward a theoretical research model of factors affecting continuance intention of mobile reading. Using 186 valid data collected through a questionnaire survey, we conducted data analysis with the partial least squares structural equation modeling （PLS-SEM）. Findings： The results show that performance expectancy, effort expectancy, social influence and perceived enjoyment all have positive impacts on continuance intention. Among them, perceived enjoyment has the most significant effect on continuance intention, followed by performance expectancy. In addition, effort expectancy significantly influences perceived enjoyment. Contrary to our expectation, facilitating conditions have no impact on continuance intention. Practical implications： This study could help mobile data service providers to foster users＇ continuous usage of mobile reading. Research limitations： This study focused only on the effect of perceived enjoyment as an internal motivation on continuance intention of mobile reading, and other possible factors were not considered. Also, continuance intention may be different from the actual behavior. Furthermore, data of student users was collected from one university in China, and was cross-sectional, while working samples were not considered. Originality value： This study considers the effects of both external and internal motivation on continuance intention of mobile reading. The results highlight the role of perceived enjoyment in building users＇ continuance intention of mobile reading.

Caesar’s British Expedition:What Are the Real Intentions Behind His Two Expeditions?

This paper aims to discuss the real intention behind Caesar’s two British expeditions and the evidence against the conquest theory.In Commentarii de Bello Gallico,Caesar claimed that he invaded Britain because he would like to teach the Britons a lesson for aiding the Gauls.Most modern scholars disagree that Caesar’s true intention is what Caesar has said,so they come up with their own theories.Ranzani says Caesar’s expedition is for glory,while Deutsch argues that Caesar’s expeditions are for pearls.Mitchell suggests that Caesar went to Britain for tin,while Ranzani,Schadee,Raaflaub,and Riggsby all come up with the theory of Caesar’s military and political gains.Brady is one of the few scholars who believe in what Caesar has said-to punish the Gauls.Moreover,Brady also considers Caesar’s expeditions a success since he does not think Caesar’s true goal is to conquer Britain.

消费者使用绿色快递包装的意愿研究:基于S-O-R框架的综合分析

消费者受到内外部刺激之后会影响其对绿色快递包装的使用意愿。选取S-O-R模型为主要框架,整合TPB(计划行为理论)模型和顾客满意度模型的关键变量,探讨了绿色快递包装相关的内外部刺激对消费者内部状态的影响。采用结构方程模型对在线调查问卷进行了实证检验分析。研究结果表明:消费者使用绿色快递包装的意愿受到态度和知觉行为控制的显著正向影响;内部刺激(感知价值和感知质量)和外部刺激(主观规范、媒体宣传和便利性)正向影响消费者使用绿色快递包装的态度和知觉行为控制。要提高消费者对绿色快递包装的使用意愿,需要加大对绿色快递包装的宣传力度,引导消费者形成积极的态度,强化绿色快递包装的实用功能,增强消费者行为控制信心。

User-oriented web search based on PLSA

In order to solve the problem that current search engines provide query-oriented searches rather than user-oriented ones, and that this improper orientation leads to the search engines＇ inability to meet the personalized requirements of users, a novel method based on probabilistic latent semantic analysis （PLSA） is proposed to convert query-oriented web search to user-oriented web search. First, a user profile represented as a user＇ s topics of interest vector is created by analyzing the user＇ s click through data based on PLSA, then the user＇ s queries are mapped into categories based on the user＇ s preferences, and finally the result list is re-ranked according to the user＇ s interests based on the new proposed method named user-oriented PageRank （UOPR）. Experiments on real life datasets show that the user-oriented search system that adopts PLSA takes considerable consideration of user preferences and better satisfies a user＇ s personalized information needs.

基于S-O-R理论的新型公共阅读空间用户流失行为分析——来自大众点评网用户评论

文章以大众点评网站中新型公共阅读空间的评论数据为对象,以S-O-R模型为理论基础,利用Python爬取其“中评”与“差评”的评论数据,运用内容分析法归纳得出新型公共阅读空间用户流失行为的主要因素,并构建新型公共阅读空间用户流失行为模型。在此基础上,提出提高新型公共阅读空间服务效能的相关建议,为提高新型阅读空间的服务质量,降低用户流失率提供参考。

Customization Product Design Based on User's Emotion and Needs

The paper presents a design method that ensures the ingenuity of the product form as well as the whole and exact expression of user’s needs. The key idea is to establish an automatic design system which can transform the user’s language needs into the product features in real-time. A rifle was taken as a research instance and soldiers were chosen as evaluation customers. The theory of fuzzy set and semantic difference are adopted to evaluate the relationship between user’s needs and product features as well as their alternatives. FAHP (fuzzy analytic hierarchy process) is utilized to judge the user’s satisfactory forms. This method can also be applied to other product form designs.

Use of methotrexate in inflammatory bowel disease in 2014: A User's Guide

Methotrexate has been used an immunomodulator in many autoimmune diseases,including inflammatory bowel disease. However,many physicians are unfamiliar or uncomfortable with its use in the management of inflammatory bowel disease. We summarize the data for use of methotrexate in common clinical scenarios:(1) steroid dependant Crohn's disease(CD);(2) maintenance of remission in steroid free CD;(3) azathioprine failures in CD;(4) in combination therapy with Anti-TNF agents in CD;(5) decreasing antibody formation to Anti-TNF therapy in CD;(6) management of fistulizing disease in CD; and(7) as well as induction and maintenance of remission in ulcerative colitis. An easy to use algorithm is provided for the busy clinician to access and safely prescribe methotrexate for their inflammatory bowel disease patients.

Frequent Itemset Mining of User’s Multi-Attribute under Local Differential Privacy

Frequent itemset mining is an essential problem in data mining and plays a key role in many data mining applications.However,users’personal privacy will be leaked in the mining process.In recent years,application of local differential privacy protection models to mine frequent itemsets is a relatively reliable and secure protection method.Local differential privacy means that users first perturb the original data and then send these data to the aggregator,preventing the aggregator from revealing the user’s private information.We propose a novel framework that implements frequent itemset mining under local differential privacy and is applicable to user’s multi-attribute.The main technique has bitmap encoding for converting the user’s original data into a binary string.It also includes how to choose the best perturbation algorithm for varying user attributes,and uses the frequent pattern tree(FP-tree)algorithm to mine frequent itemsets.Finally,we incorporate the threshold random response(TRR)algorithm in the framework and compare it with the existing algorithms,and demonstrate that the TRR algorithm has higher accuracy for mining frequent itemsets.

Web Search Query Privacy, an End-User Perspective

While search engines have become vital tools for searching information on the Internet, privacy issues remain a growing concern due to the technological abilities of search engines to retain user search logs. Although such capabilities might provide enhanced personalized search results, the confidentiality of user intent remains uncertain. Even with web search query obfuscation techniques, another challenge remains, namely, reusing the same obfuscation methods is problematic, given that search engines have enormous computation and storage resources for query disambiguation. A number of web search query privacy procedures involve the cooperation of the search engine, a non-trusted entity in such cases, making query obfuscation even more challenging. In this study, we provide a review on how search engines work in regards to web search queries and user intent. Secondly, this study reviews material in a manner accessible to those outside computer science with the intent to introduce knowledge of web search engines to enable non-computer scientists to approach web search query privacy innovatively. As a contribution, we identify and highlight areas open for further investigative and innovative research in regards to end-user personalized web search privacy—that is methods that can be executed on the user side without third party involvement such as, search engines. The goal is to motivate future web search obfuscation heuristics that give users control over their personal search privacy.

User’s Relevance of PIR System Based on Cloud Models

A new method to evaluate fuzzily user＇s relevance on the basis of cloud models has been proposed. All factors of personalized information retrieval system are taken into account in this method. So using this method for personalized information retrieval （PIR） system can efficiently judge multi-value relevance, such as quite relevant, comparatively relevant, commonly relevant, basically relevant and completely non-relevant, and realize a kind of transform of qualitative concepts and quantity and improve accuracy of relevance judgements in PIR system. Experimental data showed that the method is practical and valid. Evaluation results are more accurate and approach to the fact better.

A Dynamic Bandwidth Allocation Solution Based on User Behavior and Software Defined EPS

For telecommunication operators, how to improve the utilization of bandwidth resources is always a problem which worthy of study, nowadays, this issue has become more and more important, since the traffic load bursting in the mobile Internet. So the key to solving this problem is that we need to find a kind of traffic model to predict the traffic load that users need. According to the predicted traffic load to allocate bandwidth to each base station dynamically.But the traffic consumption behavior of a single base station is random, it is difficult to predict[ 1 ]. For this reason, we based on reality that, when the user moves, it may get traffic load from different base stations, therefore, there will have some kind of relationship between those base stations.We use this relationship to establish a kind of Spatial Collaborative Network.consequently, we make use of stability algorithm to divided those base station cluster into different communities, According to the traffic load usage rules which these communities indicated to us, we get a traffic model.At the same time, we studied bow to use this traffic model in the future networks to dynamically allocate bandwidth resources, then we propose a new kind of EPS architecture based on SDN, on this platform, we can deploy our strategy through it＇s programmable interface.Finally, we designed an experiment to test the performance of our dynamic strategy, and the result shows that our method enables bandwidth utilization has been greatly improved.