Literature Books Recommender System using Collaborative Filtering and Multi-Source Reviews

• Autorzy: Luțan Elena-Ruxandra , Bădică Costin

Identyfikatory

DOI

10.15439/2024F9868

• Konferencja: Federated Conference on Computer Science and Information Systems (19 ; 08-11.09.2024 ; Belgrade, Serbia)
• Języki publikacji: EN

Abstrakty

EN

In this contribution, we present a method for obtaining literature books recommendations using collaborative filtering recommender system technique and emotions extracted from multi-source online reviews. We experimentally validated the proposed system using a book dataset and associated reviews that we collected from Goodreads and Amazon websites using our customized web scrapers. We show the benefits of using multi-source reviews by proposing a series of recommender system evaluation measures, which include single-source and multi-source recommendations similarity, recommendation algorithm usecases coverage and generated recommendations relevance.

Słowa kluczowe

EN

collaborative filtering; recommender system; multi-source review; emotions extraction; book recommendation

PL

filtrowanie kolaboracyjne; system rekomendacji; recenzja; rekomendacja książki

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2024
• Tom: Vol. 39
• Strony: 225–--230
• Opis fizyczny: Bibliogr. 11 poz., tab., wykr., wz.

Twórcy

autor

Luțan Elena-Ruxandra

• Department of Computers and Information ,Technology University of Craiova, 200585, Craiova, Romania

• https://orcid.org/0000-0001-5363-9930

autor

Bădică Costin

• Department of Computers and Information ,Technology University of Craiova, 200585, Craiova, Romania

• https://orcid.org/0000-0001-8480-9867

Bibliografia

• 1. M. R. Bouadjenek, E. Pacitti, M. Servajean, F. Masseglia, and A. E. Abbadi. A distributed collaborative filtering algorithm using multiple data sources. In The Tenth International Conference on Advances in Databases, Knowledge, and Data Applications, 2018.
• 2. E. Hasan, M. Rahman, C. Ding, J. X. Huang, and S. Raza. Review-based recommender systems: A survey of approaches, challenges and future perspectives. Computer Science, 2405.05562, 2024.
• 3. H. Liu, Q. Cao, X. Huang, F. Liu, C. Zhang, and J. An. Multi-source information contrastive learning collaborative augmented conversational recommender systems. Complex and Intelligent Systems, 2024.
• 4. E.-R. Luţan and C. Bădică. Emotion-based literature book classification using online reviews. Electronics, 11(3412), 2022.
• 5. E.-R. Luţan and C. Bădică. Emotion-based literature books recommender systems. In Proceedings of the 18th Conference on Computer Science and Intelligence Systems, volume 35, pages 275–280, 2023.
• 6. E.-R. Luţan and C. Bădică. Experimenting emotion-based book recommender systems with social data. In Information Technology for Management: Solving Social and Business Problems Through IT, pages 164–182, 2024.
• 7. D. Roy and C. Ding. Multi-source based movie recommendation with ratings and the side information. Social Network Analysis and Mining, 11(76), 2021.
• 8. D. Roy and F. Shirazi. A review on multiple data source based recommendation systems. In 2021 International Conference on Computational Science and Computational Intelligence (CSCI), pages 1534–1539, 2021.
• 9. I. Schoinas and C. Tjortjis. Musif: A product recommendation system based on multisource implicit feedback. 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), pages 660–672, 2019.
• 10. A. Speciale, G. Vallero, L. Vassio, and M. Mellia. Recommendation systems in libraries: an application with heterogeneous data sources. In 7th International workshop on Data Analytics solutions for Real-LIfe Applications, 2023.
• 11. H. Toumy. Perfume project. 2019. https://hayatoumy.github.io/recommender_system/ [Accessed: (May 10, 2024)].

Uwagi

1. Main Track: Short Papers

2. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).