Emotion-Based Literature Books Recommender Systems

• Autorzy: Luțan Elena-Ruxandra , Bădică Costin

Identyfikatory

DOI

10.15439/2023F8647

• Języki publikacji: EN

Abstrakty

EN

In this paper we propose two book recommendation methods based on emotions extracted from user reviews, using content-based filtering and collaborative filtering. The methods were experimentally evaluated on our own dataset that we collected from Goodreads -- a popular website with large database of books and readers reviews. We created an experimental setup where the recommendation algorithms for carrying out the evaluation using two proposed evaluation metrics: coverage and average recommendations similarity.

Słowa kluczowe

EN

measurement; training; filtering; database; collaborative filtering; filtering algorithms; feature extraction

PL

pomiar; szkolenie; filtrowanie; baza danych; algorytm filtrowania; ekstrakcja cech

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 275--280
• Opis fizyczny: Bibliogr. 16 poz., tab., wz., il.

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. Aggarwal, C.: Recommender Systems The Textbook (2016) Springer International Publishing
• 2. Agrawal, R.: How to Build a Book Recommendation System (2021) https://www.analyticsvidhya.com/blog/2021/06/build-book-recommendation-system-unsupervised-learning-project/
• 3. Dey, V.: Collaborative Filtering vs Content-Based Filtering for Recommender Systems (2021) https://analyticsindiamag.com/collaborative-filtering-vs-content-based-filtering-for-recommender-systems/. Last accessed 10 Feb 2023
• 4. Dubey, H., Gandhimathi, S. K.: Book Recommendation System Using Deep Learning (GPT3) International Research Journal of Engineering and Technology (IRJET), vol. 9(5) (2022)
• 5. Karbhari, V.:What is a cosine similarity matrix? (2020) https://medium.com/acing-ai/what-is-cosine-similarity-matrix-f0819e674ad1. Last accessed 10 Feb 2023
• 6. Kumar, A., Chawla, S.: Framework for Hybrid Book Recommender System based on Opinion Mining. International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Vol.8(4) (2019) 10.35940/ijrte.D7518.118419
• 7. Luţan, E.-R., Bădică, C.: Emotion-Based Literature Book Classification Using Online Reviews. Electronics 2022, 11, 3412. https://doi.org/10.3390/electronics11203412
• 8. Martín, J.,Ribé, E.: BRAIN L: A book recommender system (2023) 10.48550/arXiv.2302.00653
• 9. Melville, P., Vikas, S.: Recommender systems. Encyclopedia of machine learning 1 pp. 829-838 (2010)
• 10. Movie Recommendation Model Using Cosine_Similarity and CountVectorizer: Scikit-Learn (2019) https://regenerativetoday.com/movie-recommendation-model-using-cosine_similarity-and-countvectorizer-scikit-learn/ Last accessed 31 Mar 2023
• 11. Polignano, M., Narducci, F. de Gemmis, M. Semeraro, G.: Towards Emotion-aware Recommender Systems: an Affective Coherence Model based on Emotion-driven Behaviors. Expert Systems with Applications 2021, 170, 114382, https://doi.org/10.1016/j.eswa.2020.114382
• 12. Rana, C., Jain, S. K.: Building a Book Recommender system using time based content filtering. WSEAS Transactions on Computers 11.2 (2012): 27-33.
• 13. Resnick, P., Hal R. V.: Recommender systems. Communications of the ACM 40.3 pp. 56-58 (1997)
• 14. Roy, D., Dutta, M.: A systematic review and research perspective on recommender systems. Journal of Big Data 9, 59 (2022). https://doi.org/10.1186/s40537-022-00592-5
• 15. Usman, A., Roko, A., Muhammad, A.B. Almu, A.: Enhancing Personalized Book Recommender System. Int. J. Advanced Networking and Applications, vol.14(03), pp. 5486–5492 (2022)
• 16. Zhang, S., Lau, J. H., Zhang, X. J., Chan, J., Paris, C.: Discovering Relevant Reviews for Answering Product-Related Queries. 2019 IEEE International Conference on Data Mining (ICDM) 10.1109/ICDM.2019. 00192

Uwagi

1. Main Track Short Papers

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).