Online web navigation assistant

Ali, Noaman M.; Gadallah, Ahmed M.; Hefny, Hesham A.; Novikov, Boris

doi:10.35634/vm210109

Cited by 3 publications

(3 citation statements)

References 28 publications

(32 reference statements)

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“…One of the most attractive web mining areas is web personalization, which aims to optimize service and data provided to users by monitoring their interaction history, users' profiles, preferences, geographic location, interests, etc. Therefore, the general definition of the personalization technique is the customization of system outputs by the gathered information about system users [20,21].…”

Section: Web Personalizationmentioning

confidence: 99%

Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User

et al. 2022

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Organizations analyze customers’ personal data to understand and model their behavior. Identifying customers’ gender is a significant factor in analyzing markets that help plan the promotional campaigns, determine target customers and provide relevant offers. Several techniques were developed to analyze different types of data, including text, image, speech, and biometrics, to identify the gender of the user. The method of synthesis of the profile name differs from one customer to another. Using numerical substitutions of specific letters, known as Leet language, impedes the gender identification task. Moreover, using acronyms, misspellings, and adjacent names impose additional challenges. Towards this goal, this work uses the customers’ profile names associated with submitted reviews to recognize the customers’ gender. First, we create datasets of profile names extracted from the customers’ reviews. Secondly, we introduce a dynamic pruned n-gram model for identifying the gender of the user. It starts with data segmentation to handle adjacent parts, followed by data conversion and cleaning to fix the use of Leet language. Feature selection through a dynamic pruned n-gram model is the next step with the recurrent misspelling correction using fuzzy matching. We evaluate the proposed approach on the real data collected from active web resources. The obtained results demonstrate its validity and reliability.

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Section: Web Personalizationmentioning

confidence: 99%

Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User

et al. 2022

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“…The fixed factors' use mainly worsens the result, as can be seen from the graphs (Fig. [4][5][6][7][8][9][10][11]. This may be due to the small size of the data set.…”

Section: Using Fixed Factors Users and Tasks (Designated As Uv+tv)mentioning

confidence: 99%

“…Thus, recommendation systems are an integral part of the major market players today. The recommendation system is a complex of algorithms, programs and services designed to form relevant recommendations to users regarding the object of information search [5].…”

Section: Introductionmentioning

confidence: 99%

The development of a model for the personalized learning path using machine learning methods

Morozevich¹,

Korotkikh²,

Kuznetsova³

2022

Bus. Inform.

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Today, the economy is undergoing a digital transformation. Its key barriers are a lack of qualified personnel, competencies and knowledge, as well as internal resistance in organizations. It can be overcome through quality staff development and training. An urgent problem is to build a personalized learning path. Modern research is aimed at the implementation of recommendation systems in order to select relevant material. However, these recommendations are based on digital traces; the student’s full personal profile, as well as organizational values are not considered. This study aims is to create an intelligent guide that would accompany an employee throughout his life in the organization, involving him in the learning process according to a personalized path based on a complex personal profile and reactions to educational material, training soft and hard skills in accordance with the values of the organization and the employee. Methods of system analysis, system engineering, psychodiagnostic research (the DISC model, Rowe’s “Decision-making Style” methodology, Honey and Mumford’s method of determining activity styles, psychotype test), software design and artificial intelligence (matrix factorization and neural networks) were used in this study. The study was conducted on a unique database collected as part of its implementation and consisting of educational tasks for soft skills development, plus data on their implementation by users with different soft skills profiles. An intelligent guide model has been developed and implemented as a software component for an enterprise management system. The basis consists of psychodiagnostic modules, organizational management, training and recommendations. The intelligence of the system we developed allows you to qualitatively form a personalized learning path that will involve an employee not only in the learning and development process, but also in achieving organizational goals. The organization receives T-shaped specialists who have a proactive position and are capable of self-organization by investing in the development of employees. The results of this study can be used by enterprises not only at the organizational level, but also through broadcasting in the education system to form an education ecosystem in accordance with the requirements of innovative development of a given region’s economy.

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SmartTips: Online Products Recommendations System Based on Analyzing Customers Reviews

et al. 2022

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Online customers’ opinions represent a significant resource for both customers and enterprises to extract much information that helps them make the right decision. Finding relevant data while searching the internet is a big challenge for web users, known as the “Problem of Information Overload”. Recommender systems have been recognized as a promising way of solving such problems. In this paper, a product recommendation system called “SmartTips” is introduced. The proposed model is built based on aspect-based sentiment analysis, which exploits customers’ feedback and applies the aspect term extraction model to rate various products and extract user preferences as well. Several factors were considered, including readers’ votes, aspect term frequency, opinion words’ frequencies, etc. We tested our model on benchmark datasets that are widely used, and the results show that it outperforms the baseline methods regarding the mean squared errors of generated predictions.

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Online web navigation assistant

Cited by 3 publications

References 28 publications

Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User

Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User

The development of a model for the personalized learning path using machine learning methods

SmartTips: Online Products Recommendations System Based on Analyzing Customers Reviews

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