PerSummRe

The size of Wikipedia grows exponentially every year, due to which users face the problem of information overload. We purpose a remedy to this problem by developing a recommendation system for Wikipedia articles. The proposed technique automatically generates a personalized synopsis of the article that a user aims to read next. We develop a tool, called PerSummRe, which learns the reading preferences of a user through a vision-based analysis of his/her past reads. We use an ensemble non-invasive eye gaze tracking technique to analyze user’s reading pattern. This tool performs user profiling and generates a recommended personalized summary of yet unread Wikipedia article for a user. Experimental results showcase the efficiency of the recommendation technique.

Hyperspherical Variational Co-embedding for Attributed Networks

Network-based information has been widely explored and exploited in the information retrieval literature. Attributed networks, consisting of nodes, edges as well as attributes describing properties of nodes, are a basic type of network-based data, and are especially useful for many applications. Examples include user profiling in social networks and item recommendation in user-item purchase networks. Learning useful and expressive representations of entities in attributed networks can provide more effective building blocks to down-stream network-based tasks such as link prediction and attribute inference. Practically, input features of attributed networks are normalized as unit directional vectors. However, most network embedding techniques ignore the spherical nature of inputs and focus on learning representations in a Gaussian or Euclidean space, which, we hypothesize, might lead to less effective representations. To obtain more effective representations of attributed networks, we investigate the problem of mapping an attributed network with unit normalized directional features into a non-Gaussian and non-Euclidean space. Specifically, we propose a hyperspherical variational co-embedding for attributed networks (HCAN), which is based on generalized variational auto-encoders for heterogeneous data with multiple types of entities. HCAN jointly learns latent embeddings for both nodes and attributes in a unified hyperspherical space such that the affinities between nodes and attributes can be captured effectively. We argue that this is a crucial feature in many real-world applications of attributed networks. Previous Gaussian network embedding algorithms break the assumption of uninformative prior, which leads to unstable results and poor performance. In contrast, HCAN embeds nodes and attributes as von Mises-Fisher distributions, and allows one to capture the uncertainty of the inferred representations. Experimental results on eight datasets show that HCAN yields better performance in a number of applications compared with nine state-of-the-art baselines.

Cosine similarity-based algorithm for social networking recommendation

Social media have become a discussion platform for individuals and groups. Hence, users belonging to different groups can communicate together. Positive and negative messages as well as media are circulated between those users. Users can form special groups with people who they already know in real life or meet through social networking after being suggested by the system. In this article, we propose a framework for recommending communities to users based on their preferences; for example, a community for people who are interested in certain sports, art, hobbies, diseases, age, case, and so on. The framework is based on a feature extraction algorithm that utilizes user profiling and combines the cosine similarity measure with term frequency to recommend groups or communities. Once the data is received from the user, the system tracks their behavior, the relationships are identified, and then the system recommends one or more communities based on their preferences. Finally, experimental studies are conducted using a prototype developed to test the proposed framework, and results show the importance of our framework in recommending people to communities.

Profiling Users for Question Answering Communities via Flow-Based Constrained Co-Embedding Model

In this article, we study the task of user profiling in question answering communities (QACs). Previous user profiling algorithms suffer from a number of defects: they regard users and words as atomic units, leading to the mismatch between them; they are designed for other applications but not for QACs; and some semantic profiling algorithms do not co-embed users and words, leading to making the affinity measurement between them difficult. To improve the profiling performance, we propose a neural Flow-based Constrained Co-embedding Model, abbreviated as FCCM. FCCM jointly co-embeds the vector representations of both users and words in QACs such that the affinities between them can be semantically measured. Specifically, FCCM extends the standard variational auto-encoder model to enforce the inferred embeddings of users and words subject to the voting constraint, i.e., given a question and the users who answer this question in the community, representations of the users whose answers receive more votes are closer to the representations of the words associated with these answers, compared with representations of whose receiving fewer votes. In addition, FCCM integrates normalizing flow into the variational auto-encoder framework to avoid the assumption that the distributions of the embeddings are Gaussian, making the inferred embeddings fit the real distributions of the data better. Experimental results on a Chinese Zhihu question answering dataset demonstrate the effectiveness of our proposed FCCM model for the task of user profiling in QACs.

PerSummRe

The size of Wikipedia grows exponentially every year, due to which users face the problem of information overload. We purpose a remedy to this problem by developing a recommendation system for Wikipedia articles. The proposed technique automatically generates a personalized synopsis of the article that a user aims to read next. We develop a tool, called PerSummRe, which learns the reading preferences of a user through a vision-based analysis of his/her past reads. We use an ensemble non-invasive eye gaze tracking technique to analyze user’s reading pattern. This tool performs user profiling and generates a recommended personalized summary of yet unread Wikipedia article for a user. Experimental results showcase the efficiency of the recommendation technique.

User Profiling Based on Nonlinguistic Audio Data

User profiling refers to inferring people’s attributes of interest ( AoIs ) like gender and occupation, which enables various applications ranging from personalized services to collective analyses. Massive nonlinguistic audio data brings a novel opportunity for user profiling due to the prevalence of studying spontaneous face-to-face communication. Nonlinguistic audio is coarse-grained audio data without linguistic content. It is collected due to privacy concerns in private situations like doctor-patient dialogues. The opportunity facilitates optimized organizational management and personalized healthcare, especially for chronic diseases. In this article, we are the first to build a user profiling system to infer gender and personality based on nonlinguistic audio. Instead of linguistic or acoustic features that are unable to extract, we focus on conversational features that could reflect AoIs. We firstly develop an adaptive voice activity detection algorithm that could address individual differences in voice and false-positive voice activities caused by people nearby. Secondly, we propose a gender-assisted multi-task learning method to combat dynamics in human behavior by integrating gender differences and the correlation of personality traits. According to the experimental evaluation of 100 people in 273 meetings, we achieved 0.759 and 0.652 in F1-score for gender identification and personality recognition, respectively.

Survey on Technique and User Profiling in Unsupervised Machine Learning Method

In order to generate precise behavioural patterns or user segmentation, organisations often struggle with pulling information from data and choosing suitable Machine Learning (ML) techniques. Furthermore, many marketing teams are unfamiliar with data-driven classification methods. The goal of this research is to provide a framework that outlines the Unsupervised Machine Learning (UML) methods for User-Profiling (UP) based on essential data attributes. A thorough literature study was undertaken on the most popular UML techniques and their dataset attributes needs. For UP, a structure is developed that outlines several UML techniques. In terms of data size and dimensions, it offers two-stage clustering algorithms for category, quantitative, and mixed types of datasets. The clusters are determined in the first step using a multilevel or model-based classification method. Cluster refining is done in the second step using a non-hierarchical clustering technique. Academics and professionals may use the framework to figure out which UML techniques are best for creating strong profiles or data-driven user segmentation.

User-Centric Adaptive Password Policies to Combat Password Fatigue

Today, online users will have an average of 25 password-protected accounts online, yet use, on average, 6.5 passwords. The excessive cognitive burden of remembering large amounts of passwords causes Password Fatigue. Therefore users tend to reuse passwords or recycle password patterns whenever prompted to change their passwords regularly. Researchers have created Adaptive Password Policies to prevent users from creating new passwords similar to previously created ones. However, this approach creates user frustration as it neglects users’ cognitive burden. This paper proposes a novel User-Centric Adaptive Password Policy (UCAPP) Framework for password creation and management that assigns users system-generated passwords based on a cognitive-behavioural agent-based model. The framework comprises a Password Policy Assignment Test (PassPAST), a Cognitive Burden Scale (CBS), a User Profiling Algorithm, and a Password Generator (PassGEN). The framework creates tailor-made password policies that maintain password memorability for users of different cognitive thresholds without sacrificing password strength and entropy. The framework successfully created 30-40% stronger passwords for Critical users and random (non-mnemonic) passwords for Typical users based on each individual’s cognitive password thresholds in a preliminary test.

A Model for Estimating the Posting Frequency in an Online Social Media with Incomplete Data Using Objective Determinants of Users’ Behaviour

Профилирование пользователя онлайн социальной сети включает задачу оценки частоты (интенсивности) различных действий, в частности, публикации постов. Однако в силу ресурсных ограничений, может быть доступна только неполная информация о времени публикации нескольких последних постов, полученная, например, в рамках интервью. Оценка интенсивности постинга на основании таких данных востребована при анализе индивидуального риска, связанного с использованием онлайн социальных сетей. В статье предложена расширенная байесовская сеть доверия, которая использует не только информацию о времени публикации последних постов, но и объективные данные из профиля пользователя: пол, возраст, число друзей. Для обучения и демонстрации работы модели были собраны данные о публикации постов случайных пользователей в онлайн социальной сети ВКонтакте. Расширенная структура имеет более высокое значение информационного критерия Акаике по сравнению с упрощенной. User profiling is related to the problem of estimation of frequency of certain user’s actions in an online social media, like posting. But due to limited resources the only information available may be imprecise information on several last episodes of posting, that can be gathered via an interview. The frequency of posting estimates with such limited data may be used in the individual risk assessment that is connected with the use of online social media, for example, in medicine or cybersecurity. In the paper the Bayes belief network (BBN) for this problem is constructed, that incorporates not only the limited data on times of several last posts in an online social media, but the objective data about the user’s profile: age, sex, and friends count. With the training dataset gathered via API VKontakte we estimated conditional probability tables for two expert BBN structures (existing reduced structure based only on dates of several last posts and novel extended structure with objective behavior determinants incorporated) and automatically learned the optimal structure for the training data. Both extended models (expert and learned) showed lower values of the information criteria (Akaike information criteria and bayesian information criteria). Then with the test dataset the classification problem of the true frequency value was assessed. All three models showed similar results based on accuracy, kappa and average accuracy characteristics. This result is related to the weak strength of arcs between frequency variable and objective behavior determinants. But nevertheless the use of such variables is important in the application in order to construct the comprehensive structure of the knowledge in the area of interest. The practical significance of the work lies in the possibility of applying the proposed model to assess the posting frequency in the online social network, in particular in the tasks of modeling risk in the field of public health and socio-cybersecurity.