Sentiment Analysis and Innovative Recommender System: Enhancing Goodreads Book Discovery Using Hybrid Collaborative and Content Based Filtering
Hui, Lee Choo; Keikhosrokiani, Pantea; Asl, Moussa Pourya; Isomursu, Minna; Oinas-Kukkonen, Henry (2024-05-11)
Avaa tiedosto
Sisältö avataan julkiseksi: 11.05.2026
Springer Publishing Company
11.05.2024
Hui, L.C., Keikhosrokiani, P., Asl, M.P., Isomursu, M., Oinas-Kukkonen, H. (2024). Sentiment Analysis and Innovative Recommender System: Enhancing Goodreads Book Discovery Using Hybrid Collaborative and Content Based Filtering. In: Saeed, F., Mohammed, F., Fazea, Y. (eds) Advances in Intelligent Computing Techniques and Applications. IRICT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-031-59707-7_9
https://rightsstatements.org/vocab/InC/1.0/
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of an article published in Advances in intelligent computing techniques and applications: Intelligent systems, intelligent health informatics, intelligent big data analytics and smart computing, volume 2. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-59707-7_9
https://rightsstatements.org/vocab/InC/1.0/
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of an article published in Advances in intelligent computing techniques and applications: Intelligent systems, intelligent health informatics, intelligent big data analytics and smart computing, volume 2. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-59707-7_9
https://rightsstatements.org/vocab/InC/1.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202405294043
https://urn.fi/URN:NBN:fi:oulu-202405294043
Tiivistelmä
Abstract
This study addresses the challenge of finding suitable books in the digital age of education, where information overload makes manual book selection difficult. It aims to analyze reviewer sentiment on Goodreads and develop a recommendation algorithm based on readers’ preferences. The study employs sentiment analysis and compares three recommender algorithms: Content-Based, Collaborative filtering, and Hybrid filtering. Goodreads data is collected using a web scraper, and the results indicate Hybrid filtering as the most effective model, outperforming others in metrics like RMSE, MSE, precision, and recall. Further optimization with the Apriori model can enhance Hybrid filtering’s accuracy and recommendation breadth, reducing system errors.
This study addresses the challenge of finding suitable books in the digital age of education, where information overload makes manual book selection difficult. It aims to analyze reviewer sentiment on Goodreads and develop a recommendation algorithm based on readers’ preferences. The study employs sentiment analysis and compares three recommender algorithms: Content-Based, Collaborative filtering, and Hybrid filtering. Goodreads data is collected using a web scraper, and the results indicate Hybrid filtering as the most effective model, outperforming others in metrics like RMSE, MSE, precision, and recall. Further optimization with the Apriori model can enhance Hybrid filtering’s accuracy and recommendation breadth, reducing system errors.
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