The number of new books that appear on the market makes it difficult for readers to find books that meet their needs and desires. There are many new books every year. Sometimes there are productions with the same or similar titles but with different authors or publishers. The content may be other, even though the title is identical. It may also happen that a similarly titled book is a sequel to an earlier one. Therefore, this paper will focus on a content-based book recommendation system. The book content used in the system is the book title. The system provides book recommendations with book titles that are the same or similar to the book selected by the user. The way the system works is to extract the book title feature with Term Frequency-Inverse Document Frequency (TF-IDF). Then, all book titles are compared with cosine similarity. There are two kinds of results from this book recommendation system. First, 20 books are similar to the book selected by the user, where the 20 books have a similarity level above 50%. Second, 20 books have a similarity level below 50%. Both types of results are offered to users to be selected according to their preferences.

1.
S.
Salloum
and
D.
Rajamanthri
,
Implementation and Evaluation of Movie Recommender Systems Using Collaborative Filtering
, (
Journal of Advances in Information Technology
,
2021
), vol.
12
, no.
3
,
2.
Y.-L.
Chen
,
Y.-H.
Yeh
, and
M.-R.
Ma
,
A movie recommendation method based on users’ positive and negative profiles
, (
Information Processing & Management
,
2021
), vol.
58
, no.
3
, p.
102531
3.
F.
Colace
,
D.
Conte
,
M.
De Santo
,
M.
Lombardi
,
D.
Santaniello
, and
C.
Valentino
,
A content-based recommendation approach based on singular value decomposition
, (
Connection Science
,
2022
), vol.
34
, no.
1
, pp.
2158
2176
.
4.
L. V.
Nguyen
,
T.-H.
Nguyen
, and
J. J.
Jung
,
Content-Based Collaborative Filtering using Word Embedding
, (
Proceedings of the International Conference on Research in Adaptive and Convergent Systems
,
2020
)
5.
Neogi
,
A. S.
,
K. A.
Garg
,
R. K.
Mirsha
, and
Y.K.
Dwivedi
,
Sentiment Analysis and Classification of Indian Farmer’s Protest Using Twitter Data
, (
International Journal of Information Management Data Insights)
, no 2 (November):
100019
.
6.
A. S.
Girsang
,
B.
Al Faruq
,
H. R.
Herlianto
, and
S.
Simbolon
,
Collaborative Recommendation System in Users of Anime Films
, (
Journal of Physics: Conference Series
,
2020
) vol.
1566
, no.
1
, p.
012057
7.
M. J.
Awan
et al.,
A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach
, (
Electronics
,
2021
)
8.
R.
Debgupta
,
A.
Saha
, and
B. K.
Tripathy
,
A Faster Fuzzy Clustering Approach for Recommender Systems
, (
Intelligent Computing and Communication
,
2020
) pp.
315
324
9.
A.
Falconnet
,
C. K.
Coursaris
,
J.
Beringer
,
W. V.
Osch
,
S.
Sénécal
, and
P.-M.
Léger
,
Improving User Experience with Recommender Systems by Informing the Design of Recommendation Messages
, (
MDPI
,
2023
)
10.
S.
Kumar
,
K.
De
, and
P. P.
Roy
,
Movie Recommendation System Using Sentiment Analysis from Microblogging Data
, (
IEEE Transactions on Computational Social Systems
,
2020
) vol.
7
, no.
4
, pp.
915
923
.
11.
F.
Wayesa
,
M.
Leranso
,
G.
Asefa
, and
A.
Kedir
,
Pattern-based hybrid book recommendation system using semantic relationships - Scientific Reports
, (
Nature
, 202)
12.
E.
Ahmed
, and
A.
Letta
,
Book Recommendation Using Collaborative Filtering Algorithm
, (
Book Recommendation Using Collaborative Filtering Algorithm
,
2023
)
13.
J.
Ni
,
Y.
Cai
,
G.
Tang
, and
Y.
Xie
,
Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics
, (
Applied Sciences
,
2021
) vol.
11
, no.
20
, p.
9554
.
14.
M.
Liang
and
T.
Niu
,
Research on Text Classification Techniques Based on Improved TF-IDF Algorithm and LSTM Inputs
, (
Procedia Computer Science
,
2022
) vol.
208
, pp.
460
470
.
15.
Z.
Huang
,
X.
Xu
,
J.
Ni
,
H.
Zhu
, and
C.
Wang
,
Multimodal Representation Learning for Recommendation in Internet of Things
(
IEEE Internet of Things Journal
,
2019
) vol.
6
, no.
6
, pp.
10675
10685
.
16.
Z.
Karevan
and
J. A. K.
Suykens
,
Transductive LSTM for time-series prediction: An application to weather forecasting
, (
Neural Networks
,
2020
) vol.
125
, pp.
1
9
.
17.
X.
Cai
,
Z.
Hu
,
P.
Zhao
,
W.
Zhang
, and
J.
Chen
,
A hybrid recommendation system with a many-objective evolutionary algorithm
, (
Expert Systems with Applications
,
2020
), vol.
159
, p.
113648
.
This content is only available via PDF.
You do not currently have access to this content.