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.
Saurabh Ashwinikumar Dave Reviewer
11 Oct 2024 04:48 PM
Approved
Relevance and Originality
The research article addresses a significant issue regarding the challenges of information retrieval and navigation within increasingly complex web environments. The exploration of web page recommendation systems is timely and highly relevant, especially given the rapid growth of e-business sectors. The originality of the article lies in its focus on utilizing web usage mining and pattern discovery algorithms to enhance recommendation techniques. However, the article could benefit from a more explicit comparison to existing recommendation systems to better highlight its novel contributions.
Methodology
The methodology section of the research article, while suggesting the use of analyzed and preprocessed web log data, lacks sufficient detail. It would enhance the study's credibility to provide a clear description of the data collection process, the specific algorithms used for clustering and data mining, and the criteria for evaluating the effectiveness of the recommendations. Including these details would not only clarify the approach taken but also allow for reproducibility of the results by other researchers in the field.
Validity & Reliability
The findings of the research article seem to indicate significant advancements in recommendation efficiency, yet the validity of these claims would be strengthened by empirical evidence. The article should include specific metrics or case studies that demonstrate the performance improvements of the proposed recommendation system. Additionally, discussing potential limitations, biases, or challenges encountered during the analysis would enhance the reliability and transparency of the results.
Clarity and Structure
The structure of the research article is generally logical, guiding the reader through the concepts of web page recommendation and the underlying techniques employed. However, some sections could benefit from clearer organization and headings to facilitate navigation. While the language is mostly accessible, simplifying technical jargon or providing definitions for complex terms would improve readability for a wider audience. The conclusion should also more effectively summarize the key findings and implications of the research.
Result Analysis
The article presents an analysis of the recommendation system's outcomes, indicating improvements in recommendation efficiency. However, the results could be more compelling if supported by concrete examples, performance metrics, or comparative analyses with existing systems. Furthermore, a more in-depth exploration of the implications of these results for users and website analysts would enhance the article's impact. Overall, while the findings are promising, a deeper examination of the results and their practical applications would significantly strengthen the contribution of this research.
IJ Publication Publisher
ok sir
Saurabh Ashwinikumar Dave Reviewer