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Using the Jaccard similarity method for recommendation
system of books
Khabibulla MADATOV
Urgench State University
ARTICLE INFO
ABSTRACT
Article history:
Received December 2023
Received in revised form
15 December 2023
Accepted 20 January 2024
Available online
25 February 2024
The main goal of pedagogy is to educate the young
generation to become mature, knowledgeable and well-rounded
individuals in all respects. In this regard, one of the main tasks
of the education system is to form a culture of reading among
young people, to provide them with textbooks and works of art
suitable for their age and intellectual potential. But only if
young readers read books suitable for their intellectual
potential based on their age characteristics, their knowledge,
spirituality, outlook and other positive aspects will develop. If
the students do not read the works according to their potential,
the reader will not be able to absorb fully the contents of the
work he has read, the information in the book will "weight" him.
As a result, the reader's desire to read begins to fade. Readers
should not read literature that is shallow in content,
incompatible with our national spirituality and values, moral
standards, and may have a negative impact on the education of
young people. Therefore, it is necessary to create a system of
recommending works suitable for the intellectual potential of
readers. This article examines the application of the Jaccard
similarity method to the creation of appropriate reading lists
for high school students. For this, a corpus is created on the
basis of high-class literature textbooks, and this corpus is
compared with literary works. Books with the highest similarity
results are recommended for reading. The problem was fully
solved on the basis of literature textbooks of 5th-11th grade
students and works of art in the Uzbek language.
2181-
1415/©
2024 in Science LLC.
DOI:
https://doi.org/10.47689/2181-1415-vol5-iss1-pp59-69
This is an open access article under the Attribution 4.0 International
(CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/deed.ru)
Keywords:
corpus,
token,
similarity of texts,
NLTK,
Jaccard algorithm,
set,
intersection of sets,
union of sets.
1
Candidate of physical and mathematical sciences. Urgench State University. E-mail: habi1972@mail.ru
2
Teacher, Urgench State University. Urgench, Uzbekistan. E-mail: sprsattarova@gmail.com
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Kitob tavsiya qilish tizimini yaratishda jakkard
o‘xshashlik usulidan foydalanish
ANNOTATSIYA
Kalit so‘zlar
:
korpus,
token,
matnlar o‘xshashligi,
NLTK,
Jaccard algoritmi,
to‘plam,
to‘plamlar
kesishmasi,
to‘plamlar birlashmasi.
Ushbu maqolada Jaccard o‘xshashlik usulini yuqori sinf
o‘quvchilari mutolaa qilishi uchun mos keladigan adabiyotlar
ro‘yxatini tuzishga tatbiqi haqida fikrlar bayon etiladi.Qo‘yilgan
masala 5-11-sinf
o‘quvchilarining adabiyot darsliklari va o‘zbek
tilidagi badiiy asarlar asosida to‘la tahlil qilingan.
Использование метода жаккардового подобия для
создания системы рекомендаций книг
АННОТАЦИЯ
Ключевые слова:
корпус,
токен,
сходство текстов,
НЛТК,
алгоритм Жаккара,
множество,
пересечение множеств,
объединение множеств.
Основная цель педагогики –
воспитать молодое поколение
зрелыми, знающими и всесторонне развитыми личностями.
В связи с этим одной из главных задач системы образования
является: формирование культуры чтения среди молодежи,
обеспечение ее учебниками и произведениями искусства,
соответствующими ее возрасту и интеллектуальному
потенциалу. Но только если юные читатели будут читать
книги, соответствующие их интеллектуальному потенциалу
и учитывающие их возрастные особенности, их знания,
духовность, мировоззрение и другие положительные
стороны будут развиваться. Если учащиеся будут читать
произведения, не соответствующие их возможностям, они не
смогут полностью усвоить содержание прочитанного
произведения, и информация в книге будет их «утяжелять». В
результате у читателя может угаснуть желание читать.
Читателям не следует выбирать литературу, которая
поверхностна по содержанию, несовместима с нашей
национальной духовностью и ценностями, нравственными
нормами и способна оказать негативное влияние на
образование молодежи. Поэтому необходимо создать систему
рекомендации
произведений,
соответствующую
интеллектуальному потенциалу читателей. В данной статье
рассматривается применение метода подобия Жаккара для
создания подходящих списков чтения для старшеклассников.
Для этого на основе учебников литературы высокого класса
создается корпус и этот корпус сравнивается с
литературными произведениями. Книги с наибольшим
результатом сходства рекомендуются к прочтению.
Проблема была успешно решена на основе учебников
литературы для учащихся 5
-
11 классов и произведений
искусства на узбекском языке.
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INTRODUCTION.
Based on the idea of the President of the Republic of Uzbekistan that "New
Uzbekistan starts from the threshold of the school, from the educational system", large-
scale reforms are being implemented. In particular, in order to strengthen the legal basis
for the development and support of reading culture in the Republic, 5 decisions of the
President of the Republic of Uzbekistan and the Government were adopted in 2017
–
2020,
and the development of reading culture was determined as a priority of the state policy.
Solving many problems in the field of natural language processing is based on
methods for determining the similarity of texts. Today, text similarity algorithms are the
main solution for plagiarism detection, document classification, data retrieval,
summarization and a number of other tasks. This algorithm was created by the Swiss
scientist Jaccard Paul in 1901, and with its help, issues of similarity between various
documents and text files are being determined. Jaccard similarity is actively used in ecology,
geobotany, molecular biology, bioinformatics, genomics, informatics and other fields. The
Jaccard formula has long been the standard solution for similarity problems. Jaccard
algorithm is easy to understand, it works without vectorization and not based on cosine
similarity. Using Jaccard similarity, it is possible to determine the similarity between simple
texts, sets of numbers, as well as complex types of text files. Jaccard similarity (also called
Jaccard similarity coefficient or Jaccard index) is one of the algorithms used to determine the
similarity between two sets. It tokenizes words and compares them through collections. It
can be used to measure the similarity between two objects, for example two text files. In
Python programming, Jaccard similarity is mainly used to measure the similarity between
two sets or two asymmetric binary vectors. Mathematically, calculating Jaccard similarity
simply takes the ratio of set intersection to set union.
LITERATURE REVIEW
In this section, we discuss the work done on text similarity. Uzbek and foreign
researchers are conducting a number of scientific researches on text processing in the
field of natural language processing. Because the creation of modern applications related
to natural language processing, conducting scientific research will undoubtedly be an
important factor in the development of any low-resource language. In today's era of rapid
development of computer technologies and the Internet, any user is faced with text
processing processes such as searching for textual information, categorizing them,
comparing and processing texts. One of the biggest challenges, especially when working
with a large number of documents, is finding information that matches your interest,
determining the degree of similarity between two works, and creating a glossary of large
volumes of books. These problems can be easily solved by methods of determining the
similarity of texts. In this paper [1], two new simple but effective similarity models are
developed considering all user rating vectors to classify relevant neighborhoods and
generate recommendations in less computational time. The source [2] proposes a new
class of tests for homogeneity of two independent polynomial samples. Their tests are a
natural extension of tests based on Jaccard's dissimilarity index, and the authors study the
asymptotic powers of these tests. The authors of this paper [3] proposed a method to
measure the similarity between words by using the Jaccard coefficient. Technically, they
developed the Jaccard similarity measure with the Prolog programming language to
compare the similarity between datasets. The authors of this paper [4] propose a min-max
hash method, which cuts the hashing time in half, but it has a slightly smaller difference in
pairwise Jaccard similarity estimation. In addition, the min-max hash estimator only
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involves checking pairwise equality, so it is well suited for approximate nearest-neighbor
searches. The authors of this paper [5] use three new similarity measures called Jaccard
vector similarity, Jaccard cross-correlation, and the inner product of Jaccard Frobenius
covariances for early motion detection by associating past features with future features.
The purpose of this study [6] was to find the optimal value similarity for text mining. They
used the Jaccard similarity method, a combination of Jaccard similarity, cosine similarity,
and Jaccard similarity and cosine similarity. By combining the two similarities, it was
achieved to increase the similarity value of the two names. The results of this study are
that the cosine similarity method gave the best value of closeness or similarity compared
to the Jaccard similarity and the combination of the two. The goal of the project proposed
by the authors of this article [7] was to create a tool for analyzing large amounts of data
related to large-scale social networks on the Internet. In particular, the project suggested
creating a Map Reduce program to calculate the Jaccard similarity coefficient based on
shared page changes among Wikipedia users. The program was then generalized to
compute the Jaccard similarity between objects in any arbitrary column of the dataset co-
occurring with another arbitrary column. The program was implemented in Java with the
MapReduce programming technique. The authors of this article [8] determined the
similarity of Uzbek texts using Jaccard and cosine similarity methods. The authors of the
article written by Uzbek researchers [8, 9, 10] developed a cosine similarity detection
algorithm based on TF-IDF for texts in the Uzbek language.
RESEARCH METHODOLOGY.
Jaccard similarity detection algorithm
3.2. Description of Jaccard similarity
BEGIN
Input data
sets
Convert to lowercase
Tokenization
Remove punctuation marks
Jaccard similarity calculation
:
J (sim)= |A
B| / | A
B
|
Printing the Jaccard
similarity results
END
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The Jaccard similarity index is a measure of similarity between two data sets.
Developed by Paul Jaccard, the index ranges from 0 to 1. The closer to 1, the more similar the
two data sets are. If two data sets have exactly the same elements, their Jaccard similarity
index is 1. Conversely, if they have no members in common, their similarity is 0.
Fig. 1: Given data sets
The following examples show how to calculate the Jaccard similarity index for
different data sets. Let us be given 2 sets A and B. (Fig. 1) The Jaccard similarity
(or Jaccard index) of these sets is defined as formula (1)
J = |A∩B||A
∪
B| = |A∩B||A| + |B| –
|A
∪
B| (1)
We divide this formula into two components:
1.Intersection of sets.
It calculates the embedded intersection between A and B,
shown by the yellow area in the infographic below. (Fig. 2)
Fig. 2: Intersection of sets
2.Combination of sets.
The denominator is actually a built-in combination of
A and B, shown in yellow in the figure below. (fig-3)
Fig. 3: Combination of sets
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Using the Jaccard similarity formula, we can see that the similarity statistic is the
ratio of the two visualizations above, where:
•
If both sets are identical, for example A=1, 2, 3 and B=1, 2, 3, then their
Jaccard
similarity = 1.
•
If sets A and B have no elements in common, say A=1, 2, 3 and B=4, 5, 6, then
their Jaccard similarity = 0
•
If sets A and B have elements in common, for example, A=1, 2, 3 and B=3, 4, 5,
then their
Jaccad similarity is 0≤ J
(A, B) ≤1
. will have some value in the interval.
Fig. 4: Given set
3.3 Jaccard similarity calculation.
Let's look at two sets (fig-4)
A = {1, 2, 3, 5, 7}
B = {1, 2, 4, 8, 9}
Fig. 5: Intersection of sets
Step 1:
As a first step, we need to find the intersection of sets A and B:
In this case: The intersection of sets is A∩B= {1,2}
Step 2:
In the second step, the union of sets A and B is found
.
Combination of sets:
A
∪
B= {1, 2, 3, 5, 7, 4, 8, 9}
Fig. 6: Combination of sets
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Step 3:
And the last step
–
the ratio of the number of elements in the intersection of the set
to the number of elements of their union is obtained.
𝐽(𝐴, 𝐵) =
𝑛(𝐴∩𝐵)
𝑛(𝐴)+𝑛(𝐵)−𝑛(𝐴∩𝐵)
(2)
From
formula
(2),
we
get
the
following
result
𝐽(𝐴, 𝐵) =
𝑛(𝐴 ∩ 𝐵)
𝑛(𝐴) + 𝑛(𝐵) − 𝑛(𝐴 ∩ 𝐵)
=
2
5 + 5 − 2
=
2
8
=
1
4
= 0.25
RESULTS
The Python programming language uses the NLTK library to process text through
the Jaccard algorithm, and it is performed in the following steps. The algorithm in Figure
6 is used to calculate it.
Example 1:
Below, we determine the similarity between sets of numbers using
Jaccard similarity
A = {0, 1, 2, 5, 6, 8, 9}, B = {0, 2, 3, 4, 5, 7, 9}
To calculate the Jaccard similarity between them, we first find the ratio of the
intersection of the two sets to their union.
• The same number of elements in both sets: {0, 2, 5, 9} = 4
• Elements in both sets: {0, 1, 2, 3, 4, 5, 6, 7, 8, 9} = 10
• Jaccard similarity: 4 / 10 = 0.4
So, The result of Jaccard similarity index is 0.4.
Example 2:
We determine the similarity of the following 2 sets:
C = {0, 1, 2, 3, 4, 5}, D = {6, 7, 8, 9, 10}
To calculate the Jaccard similarity between them, we first find the ratio of the
intersection of the two sets to their union.
• Same number of elements in both sets: {} = 0
• Elements in both sets: {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10} = 11
•
Thus, the result
of the Jaccard similarity: 0 / 11 = 0
The Jaccard similarity index turned out to be 0. This indicates that the two data
sets have no common elements.
Example 3: Jaccard similarity for words
We can determine the Jaccard similarity index for a data set that contains
characters as opposed to numbers.
E = {‘maktab’, ‘kitob’, ‘daftar’, ‘qalam’}
F = {‘qalam’, ‘ustoz’, ‘doska’, ’parta’}
E ={'school', 'book', 'notebook', 'pen'}
F ={'pen', 'teacher', 'blackboard', 'desk'}
To calculate the Jaccard similarity between them, we first find the ratio of the
intersection of the two sets to their union.
• Same number of elements in both sets: {'qalam)'} = 1
• Elements in both sets: {‘maktab’,
‘kitob’,
‘daftar’,
‘qalam’,
‘ustoz’,
‘doska’,
‘parta’
} = 7
•
Thus, the result
of the Jaccard similarity: 1 / 7= 0.142857
Example 4
: We have already understood the calculation of Jaccard similarity from
the above examples. Now, with the help of this similarity, we will determine the
similarity of texts in Uzbek language. Alisher Navoi's "Lison-ut-Tair" and Farididin Attar's
"Mantiq-ut-Tair" were selected for this purpose. We have already understood the
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calculation of Jaccard similarity from the above examples. Now, with the help of this
similarity, we will determine the similarity of the texts in the Uzbek language. For this
purpose, Alisher Navoi's "Lison-ut-tair" and Farididin Attor's "Mantiq-ut-tair" were
selected. The code written by the authors in Python program for calculating Jaccard
similarity is also presented in detail.
import
nltk
import
string
import
re
# Open for reading from a file in text mode
f1 =
open
(
"Navoiy.txt", "rt"
)
data1 = f1.read()
f2 =
open
(
"Attor.txt", "rt"
)
data2 = f2.read()
#Tokenization process
tokens1= nltk.word_tokenize(data1)
tokens2= nltk.word_tokenize(data2)
# Print tokens
(tokens1)
(tokens2)
#Determining the number of tokens
k1 =
len
(tokens1)
k2 =
len
(tokens2)
(
‘number of tokens in file 1 ='
,k1)
(
'number of tokens in file 2='
,k2)
A =
set
(tokens1)
B =
set
(tokens2)
def
Jaccard_similarity
(A, B):
nominator = A.intersection(B)
denominator = A.union(B)
similarity =
len
(nominator)/
len
(denominator)
return
similarity
similarity = Jaccard_similarity(A, B)
(
‘1 st file: Lison
-ut- Tayr; File 2: Mantiq-ut Tayr, jac.sim: = '
,similarity)
The result is as follows:
================RESTART:D:\PYTHON\Jaccard-disser.py =============
number of tokens in file 1= 48817
number of tokens in file 2= 65047
1 st file:Lison-ut Tayr ; 2nd file:Mantiq-ut Tayr,jac. sim: = 0.22059697942680462
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Table 1
List of sources that we investigated in this article (This list consists of school
literature textbooks and works of fiction in various genres)
№
File name
Source name
Number
of tokens
Number
of unique
words
1.
5.txt
Literature textbook for grade 5
67 168
20 667
2.
6.txt
Literature textbook for grade 6.
61 664
19 577
3.
7.txt
Literature textbook for grade 7
66 325
20 905
4.
8.txt
Literature textbook for grade 8
81 209
22 555
5.
9.txt
Literature textbook for grade 9
79 310
24 110
6.
10.txt
Literature textbook for grade 10
75 938
22 985
7.
11.txt
Literature textbook for grade 11
78 706
24 505
8.
lison.txt
Alisher Navoi's "Lison-ut-Tair".
48456
12845
9.
mantiq.txt Farididdin Attar's "Mantiq-ut-Tair
64666
17943
10.
feruz.txt
A collection of ghazals by Muhammad Rahimkhan
Feruz "Ne bolldi yorim kelmadi".
7216
2477
11.
shaytan.txt Tahir Malik's novel "Shaytanat".
124837
30258
12.
garri.txt
Harry Potter by Joanna Kathleen,
406772
34494
Table 2
Comparison of school literature textbooks:
(In the process of defining file similarities, after comparing the text from the
corresponding stage of the school bases with it, their Jaccard similarity was equal to 1,
and in the other cases, the result was 0<Jac(sim)≤1)
№
Files
5.txt
6.txt
7.txt
8.txt
9.txt
10.txt
11.txt
1.
5.txt
1.0
0.201
0.212
0.212
0.198
0.197
0.194
2.
6.txt
0.201
1.0
0.200
0.198
0.201
0.200
0.193
3.
7.txt
0.212
0.200
1.0
0.190
0.203
0.199
0.195
4.
8.txt
0.212
0.198
0.190
1.0
0.198
0.196
0.191
5.
9.txt
0.198
0.201
0.203
0.198
1.0
0.204
0.202
6.
10.txt
0.197
0.200
0.199
0.196
0.204
1.0
0.205
7.
11.txt
0.194
0.193
0.195
0.191
0.202
0.205
1.0
According to the final results, according to the sources listed in Table 1, the Jaccard
similarity algorithm of the Uzbek language texts was achieved using the Python
programming language, and the results listed in Tables 2-3 were achieved. From the
results, we can see that the same source similarity is equal to 1, while the Jaccard
similarity is equal to 0<Jac(sim)≤1 in other cases. The essence of our article is that, using
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the algorithm shown in Figure 1, we determined the Jaccard similarity of works of
different genres recommended to schoolchildren of grades 5-11. Based on the maximum
similarity result, we can conclude which class the material of the given genre
corresponds to. Using this algorithm to create a list of books for a young readers' review
gives effective results.
Table 3
Final results for book recommendation
№
Files
lison.txt
mantiq.txt
Feruz.txt
Shaytan.txt
Garri.txt
1.
5.txt
0.156
0.163
0.028
0.167
0.156
2.
6.txt
0.146
0.146
0.029
0.172
0.163
3.
7.txt
0.147
0.146
0.032
0.162
0.153
4.
8.txt
0.169
0.173
0.031
0.155
0.152
5.
9.txt
0.149
0.155
0.035
0.164
0.157
6.
10.txt
0.147
0.150
0.031
0.166
0.154
7.
11.txt
0.143
0.149
0.032
0.161
0.153
From this table, we can see that all the links except the feruz.txt file are suitable for
students of school textbooks. Feruz. txt collection of ghazals, since it consists of classical
vocabulary, we cannot recommend it for school students.
DISCUSSION
In this article, the importance of text similarity algorithms, their fields of
application, the work carried out by Uzbek and foreign scientists in this regard, and the
issue of applying the Jacquard similarity algorithm to texts in the Uzbek language were
considered. The computational algorithm and Python code program for the Jaccard
similarity method are clearly and simply explained by the authors. The main purpose of
the article was to use the jacquard similarity method to recommend a list of books that
match the intellectual potential of schoolchildren. The problem was completely solved in
the case of the educational corpus consisting of school textbooks and various works of
art. For this purpose, a corpus of 5-11th grade literature textbooks and several works
written in Uzbek language was created. They are divided into tokens, a list of unique
words is defined, and the similarity of texts is determined using Python program code
based on the created algorithm. The software code presented in this article can be used
to calculate the degree of similarity of any text document or literary source.
CONCLUSION
The main goal of pedagogy is to educate the young generation to become mature,
knowledgeable, well-rounded individuals in all respects. From this point of view, one of
the main tasks of the education system is to form a culture of reading among young
people, to provide them with textbooks and works of art suitable for their age and
intellectual potential [10]. The creation of terminological dictionaries [11] for the books
that young people read will have an effective effect on their development to become
perfect people who meet the requirements of the time. He should also create a model of
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the universe and the solar system [12] in various programming environments, and
regularly work on himself outside of class, reading works of art. Using the method
proposed by the authors of this article, it is possible to develop the reading culture of
students by creating a system of recommending works of art that match the intellectual
potential of students.
In this article, a method based on the Jaccard algorithm for the similarity of texts in
the Uzbek language using the Python program was considered. To calculate this
similarity, the similarity between numbers, words and works was calculated. The method
considered in the article is interpreted in a completely new way. Using the algorithm
proposed by the authors, it is proposed to create a system that recommends suitable
books for young readers. We believe that the information presented in the article will be
a useful resource for literary critics analyzing works of art, students studying natural
language processing, and any researcher
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