Book recommendation and meaning systems for blind persons

Document Type : Research Paper


Computer Department, College of Science, Mustansiriyah University, Baghdad, Iraq


There are many audio reading systems to help blind persons to read the texts. The reading system alone is not enough to help blind persons. This paper aims to design and implement a book recommendation and meaning system which appended to an audio reading system that was proposed previously. This book recommendation system is based on the book title that the blind person reads. In addition, the meaning system is used to get the meaning of the most frequent word in the text that is read. Since the proposed system is utilized by blind persons, it uses text-to-speech tools to convert the result into audio.


[1] R.G. Crespo, O.S. Mart´ınez, J.M.C. Lovelle, B.C.P. Garc´ıa-Bustelo, J.E.L. Gayo and P.O. De Pablos, Recommendation system based on user interaction data applied to intelligent electronic books, Comput. Human Behavior
27(4) (2011) 1445–1449.
[2] P. Devika, R.C. Jisha and G.P. Sajeev, A novel approach for book recommendation systems, IEEE Int. Conf.
Comput. Intell. Comput. Res. 2016, pp. 1–6.
[3] S. Dokhe, M. Dube, S. Gade and V. Nemade, Survey Paper: Image Reader For Blind Person, Int. Res J Engin
Technol. 5(4) (2018) 1738–1740.
[4] K. Kowsari, K. Jafari Meimandi, M. Heidarysafa, S. Mendu, L. Barnes and D. Brown, Text classification algorithms: A survey, Inf. 10(4) (2019).
[5] P. Kumar, A.J. Obaid, K. Cengiz, A. Khanna and V.E. Balas, A Fusion of Artificial Intelligence and Internet
of Things for Emerging Cyber Systems, Intelligent Systems Reference Library, vol 210, Springer, Cham. 1st ed.
2022 edition, August 24, 2021.
[6] Y. Liu, Z. Li, Z. Yin and L. Lyu, Effect of demographic structure on resource utilisation using term frequency–inverse document frequency algorithm–evidence from China, J. Engin. 2018(16) (2018) 1490–1497.
[7] P. Mathew, B. Kuriakose and V. Hegde, Book recommendation system through content based and collaborative
filtering method, Int. Conf. Data Min. Adv. Comput. 2016, pp. 47–52.
[8] D. Pathak, S. Matharia and C.N.S. Murthy, NOVA: Hybrid book recommendation engine, 2013 3rd IEEE Int.
Adv. Comput. Conf. 2013, pp. 977–982.
[9] S. Parvatikar and B. Joshi, Online book recommendation system by using collaborative filtering and association
mining, IEEE Int. Conf. Comput. Intell. Comput. Res.2015, pp. 1–4.
[10] S. Sabitha and T. Choudhury, Proposed approach for book recommendation based on user k-NN, Adv. Comput.
Computat. Sci. (2018) 543–558.
[11] N.N.A. Sjarif, N.F.M. Azmi, S. Chuprat, H.M. Sarkan, Y. Yahya and S.M. Sam, SMS Spam message detection
using term frequency-inverse document frequency and random forest algorithm, Procedia Computer Sci. 161 (2019)
[12] Y. Tian, B. Zheng, Y. Wang, Y. Zhang and Q. Wu, College library personalized recommendation system based on
hybrid recommendation algorithm, Procedia CIRP. 83 (2019) 490–494.
[13] P.C. Vaz, R. Ribeiro and D.M. de Matos, 2013, Understanding temporal dynamics of ratings in the book recommendation scenario, Proc. 2013 Int. Conf. Inf. Syst. Design Commun., 2013, pp. 11–15.
[14] P.C. Vaz, D. Martins de Matos, B. Martins and P. Calado, Improving a hybrid literary book recommendation
system through author ranking, Proce. 12th ACM/IEEE-CS joint conf. Digital Libraries, 2012, pp. 387–388.
Volume 12, Special Issue
December 2021
Pages 2019-2023
  • Receive Date: 24 October 2021
  • Accept Date: 05 December 2021
  • First Publish Date: 07 December 2021