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Integrated Personalized Book Recommendation using Social Media Analysis


Affiliations
1 Symbiosis Centre for Information Technology, Pune, India
 

Today, most e-commerce sites use product-specific recommendation systems to better user experience. The algorithm used by such sites is - item-toitem collaborative filtering. This matches each user who has purchased and rated items to similar items and then combines those similar items into a recommendation list. The solution proposed in this paper is an integrated book recommendation system that maps the user’s highly rated books with books of a similar genre, maps the interactions of said user on social media to assess the kind of books one is interested in, and considers the collaborative filtering or association mapping between the items. In this model, authors used datasets for the same Goodreads book collection, Amazon and Goodreads reviews, transaction histories, and Twitter data. The proposed solution shall use a weighted measure, k-means clustering, and sentiment analysis. The collaborative filtering will be done using the Apriori mechanism to develop an integrated book recommendation list. The result is a list of 10 books that are recommended for a particular user. The proposed model met 80 percent of the user’s expected recommendations, whereas the simple collaborative model only met 60 percent of the user’s expectations. The collaborative model consisted majority of books by the same author or of a complete contrast genre as the method only considers the choice of other similar users and not similar books. So, the proposed integrated recommendation system is more accurate in its recommendations than a simple collaborative system. This model helps firms recommend the best possible book for book lovers. It also helps book lovers to find the best content as per their interests.

Keywords

Recommendation Systems, Sentiment Analysis, Text Analytics, Data Mining, Data Science, Data Analysis
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  • Chandrasekaran, S., & Kumar De, S. (2019). Decoding User Interaction Dynamics on Facebook Fan Page: A Sentiment Mining Approach. Global Business Review, 097215091882507. https://doi.org/10.1177/0972150918825078

  • Chen, C., & Chen, A. (2007). Using data mining technology to provide a recommendation service in the digital library. The Electronic Library, 25(6), 711–724. https://doi.org/10.1108/02640470710837137

  • Chiu, D. K. W., Bellatreche, L., Sasaki, H., Leung, H. F., Cheung, S. C., Hu, H., & Shao, J. (Eds.). (2011). Web Information Systems Engineering – WISE 2010 Workshops. Lecture Notes in Computer Science. Published. https://doi. org/10.1007/978-3-642-24396-7

  • Cho, J., Gorey, R., Serrano, S., Wang, S., & Watanabe-Inouye, J. (2016). Book Recommendation System.

  • Devika, P., Jisha, R. C., & Sajeev, G. P. (2016). A novel approach for book recommendation systems. 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). Published. https://doi.org/10.1109/iccic.2016.7919606

  • Hikmatyar, M., & Ruuhwan. (2020). Book Recommendation System Development Using User-Based Collaborative Filtering. Journal of Physics: Conference Series, 1477, 032024. https://doi.org/10.1088/1742-6596/1477/3/032024

  • Kurmashov, N., Latuta, K., & Nussipbekov, A. (2015). Online book recommendation system. 2015 Twelve International Conference on Electronics Computer and Computation (ICECCO). Published. https://doi.org/10.1109/icecco.2015.7416895

  • Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: item-toitem collaborative filtering. IEEE Internet Computing, 7(1), 76–80. https://doi. org/10.1109/mic.2003.1167344

  • Mathew, P., Kuriakose, B., & Hegde, V. (2016). Book Recommendation System through content based and collaborative filtering method. 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE). Published. https://doi.org/10.1109/sapience.2016.7684166

  • Mishra, S., & Subudhi, R. N. (2019). The Methodological Domain in Management Research. Methodological Issues in Management Research: Advances, Challenges, and the Way Ahead, 1–10. https://doi.org/10.1108/978-1-78973- 973-220191001

  • Mooney, R. J., & Roy, L. (2000). Content-based book recommending using learning for text categorization. Proceedings of the Fifth ACM Conference on Digital Libraries - DL ’00. Published. https://doi.org/10.1145/336597.336662

  • Sushama Suresh Rajpurkar, Darshana Rajnikant Bhatt and Pooja Malhotra. (2015). Book Recommendation System. International Journal for Innovative Research in Science & Technology, 1(11), 314-316.

  • Obeidat, R., Duwairi, R., & Al-Aiad, A. (2019). A Collaborative Recommendation System for Online Courses Recommendations. 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep- ML). Published. https://doi.org/10.1109/deep-ml.2019.00018

  • Rana, A., & Deeba, K. (2019). Online Book Recommendation System using Collaborative Filtering (With Jaccard Similarity). Journal of Physics: Conference Series, 1362, 012130. https://doi.org/10.1088/1742-6596/1362/1/012130

  • Sase, A., Varun, K., Rathod, S., & Patil, P. D. (2015). A Proposed Book Recommender System. IJARCCE, 481–483. https://doi.org/10.17148/ijarcce.2015.42108

  • Sohail, S. S., Siddiqui, J., & Ali, R. (2013). Book recommendation system using opinion mining technique. 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI). Published. https:// doi.org/10.1109/icacci.2013.6637421

  • Subudhi, R. N. (2019). Testing of Hypothesis: Concepts and Applications. Methodological Issues in Management Research: Advances, Challenges,and the Way Ahead, 127–143. https://doi.org/10.1108/978-1-78973-973-220191009

  • Talwar, K. S., Oraganti, A., Mahajan, N., & Narsale, P. (2015). Recommendation System using Apriori Algorithm. Int. J. Sci. Res. Dev, 3(01), 183-185.

  • Tewari, A., & Barman, A. (2017). Collaborative Recommendation System Using Dynamic Content based Filtering, Association Rule Mining and Opinion Mining. International Journal of Intelligent Engineering and Systems, 10(5), 57–66. https://doi.org/10.22266/ijies2017.1031.07

  • Valois B Jr, C., & Oliveira, M. A. D. (2011). Recommender systems in social networks. JISTEM-Journal of Information Systems and Technology Management, 8(3), 681-716.

  • Vaz, P. C., Martins de Matos, D., Martins, B., & Calado, P. (2012, June). Improving a hybrid literary book recommendation system through author ranking. Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries (pp. 387-388).

  • Wadikar, D., Kumari, N., Bhat, R., & Shirodkar, V. (2020). Book Recommendation Platform using Deep Learning.


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  • Integrated Personalized Book Recommendation using Social Media Analysis

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Authors

Krishna Kumar Singh
Symbiosis Centre for Information Technology, Pune, India
Ishani Banerjee
Symbiosis Centre for Information Technology, Pune, India

Abstract


Today, most e-commerce sites use product-specific recommendation systems to better user experience. The algorithm used by such sites is - item-toitem collaborative filtering. This matches each user who has purchased and rated items to similar items and then combines those similar items into a recommendation list. The solution proposed in this paper is an integrated book recommendation system that maps the user’s highly rated books with books of a similar genre, maps the interactions of said user on social media to assess the kind of books one is interested in, and considers the collaborative filtering or association mapping between the items. In this model, authors used datasets for the same Goodreads book collection, Amazon and Goodreads reviews, transaction histories, and Twitter data. The proposed solution shall use a weighted measure, k-means clustering, and sentiment analysis. The collaborative filtering will be done using the Apriori mechanism to develop an integrated book recommendation list. The result is a list of 10 books that are recommended for a particular user. The proposed model met 80 percent of the user’s expected recommendations, whereas the simple collaborative model only met 60 percent of the user’s expectations. The collaborative model consisted majority of books by the same author or of a complete contrast genre as the method only considers the choice of other similar users and not similar books. So, the proposed integrated recommendation system is more accurate in its recommendations than a simple collaborative system. This model helps firms recommend the best possible book for book lovers. It also helps book lovers to find the best content as per their interests.

Keywords


Recommendation Systems, Sentiment Analysis, Text Analytics, Data Mining, Data Science, Data Analysis

References





DOI: https://doi.org/10.23862/kiit-parikalpana%2F2023%2Fv19%2Fi1%2F220834