Improving Web Recommendation Systems via Feature Engineering for Anticipating User's Subsequent Links

Subject Areas : Computer Engineering and IT

Vahid  Saffari ¹ (Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran)
Karamolah  BagheriFard ² (Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran )
Hamid  Parvin ³ (3 Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran )
Samad   Nejatian ⁴ (Department of Electrical Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran)
Vahide  Rezaie ⁵ (Mathematics Dept., Yasooj Branch, Islamic Azad University, Yasooj, Iran)

Received: 2024-01-03 Accepted : 2024-03-07 Published : 2024-04-02

Keywords: Web mining, Future Engineering, User Behavior Modeling, Next Web Page Prediction,

Abstract :

Given the remarkable growth in online content and extensive user engagement, understanding user behavior and providing accurate content recommendations stands as a significant challenge in data mining and recommendation systems. This article introduces a comprehensive approach to enhance user profiling accuracy and increase precision in web page recommendations. It initiates this process by introducing an innovative feature called "user engagement duration with web pages," significantly aiding in improving user profiles. Leveraging these enriched profiles facilitates predicting a user's next web page visit. Evaluating this model, comparison with a scenario lacking this new feature demonstrates a substantial increase in prediction accuracy upon its inclusion. Additionally, we delve into cluster analysis, employing k-means and k-medoids algorithms, where k-medoids demonstrate greater diversity in sample clustering. The paper establishes the superiority of using k-medoids in this domain and emphasizes the importance of determining optimal cluster sizes. Ultimately, this research culminates in developing a web recommendation system capable of highly accurate predictions regarding the user's next web destination. Hence, the proposed approach enhances the model's precision in recommending links to users and promises further advancements in this field.

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