Efficient Pairwise Association Rules for Personalized Recommendations: Leveraging Caching and Asynchronous Model Updates

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Mortazavi Seyed Mohammad; Feyzi Farid

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Computational Sciences and Engineering Year:2024 | Volume:4 | Issue:2 Page(s): 237-257

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

Efficient Pairwise Association Rules for Personalized Recommendations: Leveraging Caching and Asynchronous Model Updates

Author(s)

Mortazavi Seyed Mohammad | Feyzi Farid | Issue Writer Certificate

Keywords

recommender system

cold start problem

Cache

asynchronous programming

improved association rules

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.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans