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.
Sandhyarani Ganipaneni Reviewer
11 Oct 2024 04:43 PM
Approved
Relevance and Originality
The paper addresses a significant issue in the digital landscape—enhancing user experience through effective web page recommendations. As websites grow in complexity and size, the need for efficient navigation tools becomes increasingly pertinent, especially in e-business sectors. The integration of artificial intelligence (AI) and data mining techniques into web page recommendations presents an original approach that aligns well with current trends in personalized user experiences. To strengthen its originality, the paper could incorporate recent case studies or examples of successful implementations in various industries, showcasing the practical benefits of the proposed system.
Methodology
The paper outlines a recommendation system that leverages analyzed and preprocessed web log data, but it lacks detail regarding the specific methodologies employed. A clearer description of the data collection process, preprocessing steps, and the clustering and data mining techniques utilized would enhance the methodological rigor. It would be beneficial to specify which data mining algorithms were used and how they were selected for this study. Including a comparative analysis of existing recommendation techniques and their limitations would also provide context for the proposed system's improvements.
Validity & Reliability
While the paper claims to demonstrate improved recommendation efficiency, it should provide empirical data or case studies to validate its claims. Discussing the reliability of the data sources used, as well as potential biases in the web log data, would contribute to a more thorough understanding of the results. Addressing limitations, such as challenges in accurately capturing user behavior or data sparsity issues, would add depth to the validity of the findings. Furthermore, incorporating user feedback or evaluation metrics to measure the effectiveness of the recommendations would bolster the reliability of the conclusions drawn.
Clarity and Structure
The paper is generally well-structured, with a logical flow from problem identification to proposed solutions. However, some sentences are lengthy and complex, which may hinder readability. Simplifying language and breaking down complex ideas into smaller, digestible sections would improve clarity. Additionally, the use of bullet points or tables to summarize key findings, patterns, or algorithms would enhance visual clarity and make the paper more accessible to a broader audience. Defining technical terms and acronyms upon their first mention would further aid comprehension.
Result Analysis
The analysis of results is presented but could be enhanced by including specific metrics or quantitative results that demonstrate the effectiveness of the proposed recommendation system. Metrics such as precision, recall, and user satisfaction ratings would provide a clearer picture of how the system performs in real-world scenarios. Furthermore, discussing potential future research directions or improvements to the recommendation system, such as incorporating user feedback loops or addressing scalability challenges, would enrich the analysis and provide a roadmap for continued development in this area.
IJ Publication Publisher
thankyou madam
Sandhyarani Ganipaneni Reviewer