Feature-Oriented Analysis of User Profile Completion Problem

2017 ◽  
pp. 304-316 ◽  
Author(s):  
Morteza Haghir Chehreghani
Keyword(s):  
User Profile ◽  
Completion Problem

EVALUATION OF THE SPATIAL DEVELOPMENT AND CURRENT USER PROFILE OF ANKARA KHANS REGION

2019 ◽  
Vol 20 (1) ◽  
pp. 176-201
Author(s):  
ZEYNEP GOKCE CAKIR ◽  
GULIZ BILGIN ALTINOZ ◽  
BURCU H OZUDURU
Keyword(s):  
User Profile ◽  
Spatial Development ◽  
Current User

Comparing ten WCAG tools for accessibility evaluation of websites

2021 ◽  
pp. 1-23
Author(s):  
Shashank Kumar ◽  
Jeevitha Shree DV ◽  
Pradipta Biswas
Keyword(s):  
User Interaction ◽  
Web Accessibility ◽  
User Profile ◽  
Syntactic Analysis ◽  
Color Contrast ◽  
Careful Evaluation ◽  
Accessibility Issues ◽  
Web Developers ◽  
Compare And Contrast ◽  
Common User

BACKGROUND: Web accessibility is one of the most important aspects of building a website. It is important for web developers to ensure that their website is accessible according to WCAG standards for people with different range of abilities. There is plethora of tools for ensuring conformance to WCAG standards but not many studies compared performance of automatic WCAG tools. OBJECTIVE: This paper compares a set of ten WCAG tools and their results in terms of ease of comprehension and interpretation by web developers. We proposed a Common User Profile format to help personalize contents of website making it accessible to people with different range of abilities. METHODS: We selected ten WCAG tools from World Wide Web Consortium (W3C) to evaluate landing pages of two popular websites. For each webpage, we identified accessibility issues and recommended alternate suggestions to help developers improve accessibility. Further, we highlighted accessibility issues that cannot be captured only through conformance to WCAG tools; and proposed additional methods to evaluate accessibility through an Inclusive User Model. We then demonstrated how simulation of user interaction can capture usability and accessibility issues that are not detected through only syntactic analysis of websites’ content. Finally, we proposed a Common User Profile format that can be used to compare and contrast accessibility systems and services, and to simulate and personalize interaction for users with different range of abilities. RESULTS AND CONCLUSIONS: After careful evaluation of two websites using the ten tools, we noted that, both websites lacked color contrast between background and foreground; lack of sign language alternatives; opening of pop-ups without proper warnings and so on. Further, results from comparative analysis of selected web accessibility tools noted that, there is no single tool that can be found ideal in all aspects. However, from our study, Utilitia Validator by Utilitia SP. z O.O was considered the most feasible tool. By rectifying and incorporating issues and alternate suggestions by simulation study and Common User Profile format respectively, developers can improve both websites making it accessible to maximum audience.

scholarly journals The Totally Positive Completion Problem: The 3-by-n Case

2021 ◽  
Vol 9 (1) ◽  
pp. 226-239
Author(s):  
D. Carter ◽  
K.E. DiMarco ◽  
C.R. Johnson ◽  
L. Wedemeyer ◽  
Z. Yu
Keyword(s):  
Completion Problem ◽  
Totally Positive

Abstract The 3-by-n TP-completable patterns are characterized by identifying the minimal obstructions up to natural symmetries. They are finite in number.

scholarly journals Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 556
Author(s):  
Thaer Thaher ◽  
Mahmoud Saheb ◽  
Hamza Turabieh ◽  
Hamouda Chantar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Selection ◽  
Language Processing ◽  
User Profile ◽  
Vital Role ◽  
Classification Model ◽  
Fake News ◽  
False Information ◽  
Social Media Platforms

Fake or false information on social media platforms is a significant challenge that leads to deliberately misleading users due to the inclusion of rumors, propaganda, or deceptive information about a person, organization, or service. Twitter is one of the most widely used social media platforms, especially in the Arab region, where the number of users is steadily increasing, accompanied by an increase in the rate of fake news. This drew the attention of researchers to provide a safe online environment free of misleading information. This paper aims to propose a smart classification model for the early detection of fake news in Arabic tweets utilizing Natural Language Processing (NLP) techniques, Machine Learning (ML) models, and Harris Hawks Optimizer (HHO) as a wrapper-based feature selection approach. Arabic Twitter corpus composed of 1862 previously annotated tweets was utilized by this research to assess the efficiency of the proposed model. The Bag of Words (BoW) model is utilized using different term-weighting schemes for feature extraction. Eight well-known learning algorithms are investigated with varying combinations of features, including user-profile, content-based, and words-features. Reported results showed that the Logistic Regression (LR) with Term Frequency-Inverse Document Frequency (TF-IDF) model scores the best rank. Moreover, feature selection based on the binary HHO algorithm plays a vital role in reducing dimensionality, thereby enhancing the learning model’s performance for fake news detection. Interestingly, the proposed BHHO-LR model can yield a better enhancement of 5% compared with previous works on the same dataset.

scholarly journals User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh B. Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
Correlation Coefficients ◽  
User Profile ◽  
Profile Data ◽  
Similarity Algorithm ◽  
Previous Algorithm ◽  
Movie Recommendation ◽  
Recommendation Accuracy

AbstractCollaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

A method of query expansion based on topic models and user profile for search in folksonomy

2021 ◽  
pp. 1-11
Author(s):  
Zhinan Gou ◽  
Yan Li
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Information Overload ◽  
Topic Model ◽  
User Profile ◽  
Expansion Method ◽  
Collaborative Tagging ◽  
Search Query ◽  
Tagging System ◽  
The Web

With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

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