چکیده مقاله
Recommender systems are recognized as essential tools for enhancing user experience and increasing engagement in e commerce platforms However, traditional methods, such as content based filtering and collaborative filtering, face challenges like the cold start problem and limited diversity in recommendations This research presents a hybrid recommender system that leverages two distinct approaches to improve recommendation accuracy The system employs textual analysis of products using the TF IDF method and cosine similarity to identify similar products, and these results are combined with collaborative filtering, which analyzes user interactions To integrate the results, a neural network model dynamically calculates final scores for hybrid recommendations The proposed system was evaluated using real world data from an online retail dataset, demonstrating that the hybrid model outperforms standalone methods In addition to improving the accuracy and quality of recommendations, the system provides more diverse and relevant suggestions, leading to increased customer satisfaction and enhanced marketing strategies The findings of this study emphasize the advantages of hybrid systems and their potential to optimize sales and enhance the online shopping experience
کلیدواژهها
نویسندگان
Rashed Shahabi
Bachelor of Computer Engineering, University of Bojnord
Elham Hajian
Assistant Professor, Department of Computer Engineering, University of Bojnord
شیوه ارجاع
Shahabi, Rashed and Hajian, Elham,1403,Enhancing E-Commerce Recommender Systems by Integrating Content-Based Filtering, Collaborative Filtering, and Neural Network Techniques,The 14th ECDC2025 international e-commerce conference with the approach of artificial intelligence, Internet of Things, business and metaverse,Shiraz
ارائهشده در
مجموعه مقالات چهاردهمین کنفرانس بین المللی تجارت الکترونیک ECDC۲۰۲۵ با رویکرد هوش مصنوعی، اینترنت اشیاء، کسب و کار و متاورس25 بهمن 1403 · شیراز