DOI QR코드

DOI QR Code

Development of the Recommender System of Arabic Books Based on the Content Similarity

  • Alotaibi, Shaykhah Hajed (Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU)) ;
  • Khan, Muhammad Badruddin (Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU))
  • 투고 : 2022.08.05
  • 발행 : 2022.08.30

초록

This research article develops an Arabic books' recommendation system, which is based on the content similarity that assists users to search for the right book and predict the appropriate and suitable books pertaining to their literary style. In fact, the system directs its users toward books, which can meet their needs from a large dataset of Information. Further, this system makes its predictions based on a set of data that is gathered from different books and converts it to vectors by using the TF-IDF system. After that, the recommendation algorithms such as the cosine similarity, the sequence matcher similarity, and the semantic similarity aggregate data to produce an efficient and effective recommendation. This approach is advantageous in recommending previously unrated books to users with unique interests. It is found to be proven from the obtained results that the results of the cosine similarity of the full content of books, the results of the sequence matcher similarity of Arabic titles of the books, and the results of the semantic similarity of English titles of the books are the best obtained results, and extremely close to the average of the result related to the human assigned/annotated similarity. Flask web application is developed with a simple interface to show the recommended Arabic books by using cosine similarity, sequence matcher similarity, and semantic similarity algorithms with all experiments that are conducted.

키워드

참고문헌

  1. Burke, Robin & Felfernig, Alexander & H. Goker, Mehmet. (2011). Recommender Systems: An Overview. Ai Magazine. 32. 13-18. 10.1609/aimag.v32i3.2361.
  2. Khusro, Shah & Ali, Zafar & Ullah, Irfan. (2016). Recommender Systems: Issues, Challenges, and Research Opportunities. 10.1007/978-981-10-0557-2_112.
  3. Bhatnagar, V. (2017). Collaborative filtering using data mining and analysis. Hershey, PA: Information Science Reference, An imprint of IGI Global.
  4. Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-to-ite m collaborative filtering. IEEE Internet Computing, 7(1), 76-80. doi:10.1109/mic.2003.1167344
  5. Gomez-Uribe, Carlos & Hunt, Neil. (2015). The Netflix Recommender System. ACM Transactions on Management Information Systems. 6. 1-19. 10.1145/2843948.
  6. FERNANDEZ, L. E., M. (2018). Recommendation System for Netflix. Retrieved from https://beta.vu.nl/nl/Images/werkstukfernandez_tcm235-874624.pdf
  7. Liu, Jiahui & Dolan, Peter & Pedersen, Elin. (2010). Personalized news recommendation based on click behavior. International Conference on Intelligent User Interfaces, Proceedings IUI. 31-40. 10.1145/1719970.1719976.
  8. Okon, Emmanuel & Eke, Bartholomew & Oghenekaro Asagba, Prince. (2018). An Improved Online Book Recommender System using Collaborative Filtering Algorithm. 10.13140/RG.2.2.24240.46086.
  9. Ali, Z., Khusro, S., & Ullah, I. (2016). A Hybrid Book Recommender System Based on Table of Contents (ToC) and Association Rule Mining. Proceedings of the 10th International Conference on Informatics and Systems - INFOS 16. doi:10.1145/2908446.2908481
  10. 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). doi:10.1109/sapience.2016.7684166
  11. Abu Samra, Y. K. (2017). Tag Recommendation for Short Arabic Text by Using Latent Semantic Analysis of .الجامعة الإسلامية - غزة , , , .Wikipedia http://hdl.handle.net/20.500.12358/20061
  12. ALMALAHMEH, T. M. (2014). SEMANTIC RECOMMENDER SYSTEM FOR MALAYSIAN TOURISM INDUSTRY. Retrieved from http://studentsrepo.um.edu.my/4646/1/TIRAD_MOHAMMED_AREF_ALMALAHMEH.pdf
  13. Dou, Y., Yang, H., & Deng, X. (2016). A Survey of Collaborative Filtering Algorithms for Social Recommender Systems. 2016 12th International Conference on Semantics, Knowledge and Grids (SKG). doi:10.1109/skg.2016.014
  14. Chandak, M., Girase, S., & Mukhopadhyay, D. (2015). Introducing Hybrid Technique for Optimization of Book Recommender System. Procedia Computer Science, 45, 23-31. doi:10.1016/j.procs.2015.03.075
  15. Worked out example: Item based Collaborative filtering for Recommender Engine. (2014, December 30). Retrieved from https://ashokharnal.wordpress.com/2014/12/18/worked-outexample-item-based-collaborative-filtering-forrecommenmder-engine/
  16. Algorithms. (n.d.). Retrieved from http://www.cs.carleton.edu/cs_comps/0607/recommend/recommender/itembased.html