Dynamic Web Personalization Using a Hybrid Recommender System with Sequential Pattern Detection and Link Sequence Similarity
محورهای موضوعی : International Journal of Data Envelopment Analysis
Vahid Saffari
1
,
Karamolah BagheriFard
2
,
Hamid Parvin
3
,
Samad Nejatian
4
,
Vahide Rezaie
5
1 - Department of Computer Engineering, Islamic Azad University, Yasooj, Iran
2 - Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran
3 - Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran
4 - دانDepartment of Electronic Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran شگاه آزاد اسلامی واحد یاسوج
5 - Department of Mathematics Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran
کلید واژه: Web Recommender System, Website Personalization, Sequential Pattern Discovery, Link Sequence Similarity, Hybrid Model,
چکیده مقاله :
In this paper, we propose a dynamic hybrid web recommender system aimed at personalizing websites through sequential pattern discovery and link sequence similarity detection. The system is evaluated on two standard datasets, Zanbil and NASA, containing extensive web server logs. After preprocessing the logs by removing irrelevant data and segmenting user interactions into sessions, we perform user clustering using the PAM algorithm with three similarity metrics: Levenshtein Distance, Longest Common Subsequence (LCS), and Needleman-Wunsch (NW). The optimal number of clusters is determined through evaluation of Precision, Recall, and F-measure, with the best results found at 350 clusters for Zanbil and 500 for NASA.
User profiles are generated using FP-Growth and SPADE, which help in identifying frequent navigation patterns. The model is then evaluated, yielding optimal Precision of 0.91 and Recall of 0.83 for SPADE combined with LCS. Results show that this combination produces the best performance, effectively capturing user behavior and providing superior personalized recommendations.
The study demonstrates that this hybrid approach enhances the personalization of web, delivering more relevant suggestions to users based on their previous interactions.
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