Document Type : Original Article

Authors

• Seyed Mohammad Mortazavi ¹
• Farid Feyzi ²

¹ Ahrar Institute of Technology and Higher Education

² University of Guilan

Abstract

Recommender systems based on content-based and collaborative filtering techniques face significant challenges, including the cold-start problem and privacy concerns due to their reliance on user profiles and product metadata. This study presents an optimized pairwise association rules (PAR) algorithm that addresses these limitations by operating independently of personal user data while maintaining recommendation accuracy. The proposed solution incorporates three key enhancements: (1) a privacy-preserving design using only transactional co-occurrence patterns, (2) a caching mechanism for modular training models that reduces recommendation latency by up to 102%, and (3) asynchronous execution for efficient resource management. Evaluations on a dataset of 20,000 food items demonstrate the algorithm's effectiveness, showing 18.7% higher nDCG scores than conventional methods while maintaining sub-second response times even with large-scale catalogs. The PAR algorithm proves particularly robust in sparse-data scenarios and cold-start conditions, offering a practical alternative to traditional approaches. Future research directions include extending the framework to cross-domain recommendations and dynamic preference modeling, further advancing the development of ethical and efficient recommender systems that balance personalization with privacy protection.

Keywords

• recommender system
• cold start problem
• cache
• asynchronous programming
• improved association rules