Transparent Peer Review By Scholar9
Effective Web Pages Recommendation System Using Artificial Intelligence And Data Mining Algorithms
Abstract
Internet and web applications provide rich platform for information search. Websites are growing in size by containing huge amount of data. This situation makes the information search and website navigation a hard task. Most of the websites are larger and complex in their structure. On the other side, e-business sectors are continuously growing and acquiring the users in web-based business environment. Therefore, it is mandatory to develop tools and techniques that assist the website visitors to achieve their target in an easy manner. It is the job of website analyst and getting the right information from the web becomes harder for many users. One possible approach to solve this problem is web page recommendation which predicts the future navigation behavior of the users and helps the users to reach their destination. The recommendation techniques collaborative filtering and content-based filtering techniques suffers from its drawbacks. So, web usage mining and pattern discovery algorithms are playing an essential role to address the problem of recommendation techniques. The purpose of this paper is to make Web page recommendations by using analyzed and preprocessed Web log data. In this way, the concept of clustering and data mining are applied to recognize the patterns. This recommendation system presents Web page recommendations to the users by examining their navigational patterns and It also provides appropriate recommendations to cater to present requirements of users. Along with, the investigational outcomes show an important development in the recommendation efficiency of the system. The objective of effective web pages recommendation system using artificial intelligence and data mining algorithms is to understand users' navigation behavior and to recommend the web pages of users' interests at a shorter span of time. This paper is to analyze and understand an efficient system that would ensure effective recommendation of web pages to the users through the assistance of data mining as well as with the integration of artificial techniques.
Priyank Mohan Reviewer
11 Oct 2024 04:58 PM
Approved
Relevance and Originality
The research article is highly relevant, addressing a significant challenge faced by users in navigating increasingly complex websites. As e-business continues to grow, the need for efficient information retrieval tools becomes critical. The originality lies in its focus on utilizing web usage mining and pattern discovery algorithms, which represent a progressive approach to enhancing user experience through predictive recommendations. However, to increase originality, the paper could explore novel applications or case studies that demonstrate unique implementations of these technologies.
Methodology
The article outlines a framework for web page recommendation by analyzing and preprocessing web log data, which is a sound methodology. However, the paper lacks detailed information about the specific algorithms used, data sources, and preprocessing techniques applied. A more comprehensive explanation of the methodology, including the criteria for selecting data and the rationale behind choosing certain algorithms, would improve the rigor of the study. Additionally, discussing how the effectiveness of the recommendation system is measured could strengthen the methodology section.
Validity & Reliability
The findings appear valid, as they are grounded in the analysis of web log data and established algorithms for clustering and data mining. However, the article would benefit from a more thorough discussion of the reliability of the data sources used, including any potential biases or limitations. Addressing issues such as sample size, the representativeness of the web log data, and the consistency of results across different contexts would enhance the validity of the conclusions drawn.
Clarity and Structure
The article is generally well-structured, with a clear progression from the problem statement to proposed solutions. However, certain sections could be clearer, particularly the descriptions of technical concepts related to data mining and clustering. Simplifying the language or providing definitions for complex terms would aid comprehension. Additionally, using diagrams or flowcharts to illustrate the recommendation process could enhance clarity and visual appeal.
Result Analysis
The article discusses the effectiveness of the recommendation system but provides limited quantitative results or metrics to substantiate its claims. Including specific performance indicators, such as accuracy rates, user satisfaction scores, or comparative analyses with existing recommendation techniques, would strengthen the result analysis. Furthermore, exploring user feedback or case studies showcasing improved navigation experiences would enrich the discussion and demonstrate practical implications of the proposed system.
IJ Publication Publisher
thankyou sir
Priyank Mohan Reviewer