Netflix Recommendation System based on TF-IDF and Cosine

Similarity Algorithms

Mohamed Chiny

, Marouane Chihab

, Omar Bencharef

and Younes Chihab

Laboratory of Computer Sciences, Ibn Tofail University, Kenitra, Morocco

Department of Computer Sciences, Cadi Ayyad University, Marrakesh, Morocco

Keywords: Netflix, EDA, Recommendation, NLP, TF-IDF, Cosine similarity

Abstract: Netflix is the most popular on-demand broadcast platform available today. Its service is available in 190

countries and includes movies and TV shows. In our study, we conducted an exploratory analysis of data

obtained from Flixable, which is a search engine that lists the content available on Netflix. A dataset of 7,787

unique records was analyzed to highlight essential information about the content available on this platform.

We also implemented a recommendation system using the TF-IDF and Cosine similarity algorithms, which

are models widely used in Natural Language Processing (NLP). The exploratory analysis has revealed

interesting data on the current trends of the content delivered on Netflix. Despite the limitation of the

recommendation system in its current state, it looks promising when additional features are taken into

consideration.

1 INTRODUCTION

Streaming services have become increasingly

important and often promising gateways to unlimited

cultural and entertainment content, as is the case with

Netflix (Colbjornsen, 2020).

Netflix is a company founded in 1997. It is

specialized in the distribution and exhibition of film

and television works through a dedicated platform.

This platform offers an online subscription rental

service for movies and television series. It allows

subscribers to access programs for a fixed monthly

fee. It lists the programs that the customer wishes to

see in their "queue" as a recommendation (Bennett,

2007).

Netflix has become one of the world's leading

entertainment services with 204 million paid

subscriptions in more than 190 countries, making

television series, documentaries and feature films

available in a wide variety of genres and languages.

However, 60% of the movies rented by Netflix are

selected based on personalized recommendations.

Indeed, a recommendation system could not only

generate more direct revenue but also additional

https://orcid.org/0000-0001-6293-7606

https://orcid.org/0000-0002-9458-0389

https://orcid.org/0000-0003-0031-7609

revenue by introducing users to new categories (Dias,

2008). Therefore, a referral system can have a

significant impact on a company's revenues (Chiny,

2021; Dawei, 2020).

As the information industry and the Internet

develop rapidly, the use of Big Data is entering

people's minds and attracting attention. It gives rise to

the recommendation system that allows the desired

information to be quickly extracted from excessive

details. In the recommendation system, the user-

based collaborative filtering algorithm has become a

search hotspot (Tie-min, 2020; Zhang, 2019).

Therefore, it is evident that Netflix prioritizes one

type of content more than others, based on user

recommendations and on the viewing rate of on-

demand broadcasts.

In our study, we conducted an exploratory study

of data regarding television and film programs

available on Netflix. The data set is collected from

Flixable (Flixable, 2021), a third party search engine

of Netflix. Indeed, in 2018, they published an

interesting report (Netflix, 2018) which shows that

the number of movies on the streaming service has

decreased by more than 2000 titles since 2010, while

Chiny, M., Chihab, M., Bencharef, O. and Chihab, Y.

Netﬂix Recommendation System based on TF-IDF and Cosine Similarity Algorithms.

DOI: 10.5220/0010727500003101

In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning (BML 2021), pages 15-20

ISBN: 978-989-758-559-3

the number of TV shows on Netflix has almost

tripled. It would be interesting to explore all the other

information that can be obtained from the same set of

data. On the other hand, we have implemented a

Netflix content recommendation system based on

similarity analysis using Term Frequency - Inverse

Document Frequency and Cosine Similarity

algorithms. Initially, the explored textual corpus

consists only of the title and the description of the

work. However, we plan to improve our system by

considering other determining features, in this case,

the demographics of the users.

The particularity of our system is its simplicity

and the reduced volume of data needed for training,

which means that it can be implemented and used in

a short time.

In the rest of this paper, we will deal with Related

works, the methodology adopted for the study, the

exploration of the results and then the discussion and

conclusion.

2 RELATED WORKS

Recommender systems are trained to suggest fast and

relevant results to users. In the existing literature,

many works have tried to produce efficient

recommendation engines using Term Frequency -

Inverse Document Frequency and Cosine Similarity.

This is the case of the hybrid recommendation system

presented to the user via a web interface. This system

uses a small data model (Muthurasu et al., 2019) or

the image-based recommendation system to guide

users to movies and TV shows on streaming

platforms (Mehta et al., 2021). Another

recommendation system that improves user

experience has been proposed by adopting a Machine

Learning-based approach and Cosine Similarity.

Popularity, genre as well as content are considered for

the design of this tool (Singh et al., 2020).

3 METHODOLOGY

Our approach is to understand Netflix's current trend

in the type of offered programs by conducting an

exploratory data analysis (EDA). Second, we set up a

system of emission recommendations based on two

techniques derived from NLP, which are TF-IDF and

Cosine Similarity.

3.1 Data Collection

The data for this study were obtained from

Flixable (Flixable, 2021), which is a search engine

that lists content available on Netflix. The dataset

contains records in 12 columns, including the title of

the show, the category to which it belongs (film or TV

show), the name of the director and main actors, the

name of the countries where the show is available for

viewing on Netflix, the rating on Netflix, the duration

of the show and its description.

3.2 Data Preprocessing

Before proceeding with the exploratory analysis of

the data, the data were filtered to exclude missing or

inconsistent information. This step resulted in 7787

unique records ready for use. Concerning the second

part, which consists of implementing a

recommendation system based on TF-IDF and Cosine

Similarity, we proceeded with tokenization and then

excluded stop words and undesirable characters such

as punctuation marks.

3.3 Exploratory Data Analysis (EDA)

Exploratory data analysis was performed using the

Python language through Jupyter Notebooks. A set of

software libraries specialized in EDA were used, such

as NumPy, Pandas, Seaborn and Matplotlib. An

essential step of the analysis is to generate the word

cloud by calculating their density through the Word

Cloud library, which is part of an NLP software stack.

For the recommendation part, we used the TF-IDF

and Cosine Similarity algorithms that we applied to

the titles and the descriptions of the programs

available on Netflix to analyze their similarities.

3.3.1 Term Frequency – Inverse Document

Frequency (TF-IDF)

The TF-IDF algorithm is used to evaluate the

importance of words in a textual corpus. The

importance is proportional to the number of times the

words appear in the document and inversely

proportional to the frequency of words appearing in

the corpus (Kang, 2016; Guo, 2016; Shengqi, 2020).

TF represents the frequency of words, indicating

the number of times they appear in a corpus (func 1).

This consists of calculating the number of word

appearances out of the total number of words present

in the corpus.

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IDF is the measure of the importance of the term

in the corpus as a whole. It consists in calculating the

logarithm of the inverse of the proportion of

documents in the corpus that contain the term (func

2). This consists of calculating the total number of

documents contained in the corpus over the number

of documents where the word is present. The

logarithm of this result constitutes the value of the

IDF. The weight of TF-IDF is calculated by

multiplying the two measures (tf

* idf

), so the greater

the weight, the more significant the word concerned

in the corpus.

3.3.2 Cosine Similarity

The cosine similarity algorithm measures the

similarity by using the cosine angle between two

vectors A and B, which can represent words,

sentences, paragraphs or even entire documents (func

3). The closer the value of the cosine to 1, the smaller

the angle between the two vectors. If we apply this

algorithm to textual corpora, then it can evaluate the

level of similarity between documents (Yunxiang,

2020).

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4 RESULTS

4.1 Exploratory Data Analysis

In the following, we present the results of our

exploratory analysis of the data retained after the pre-

processing stage.

4.1.1 Ranking of Countries According to the

Number of Emissions Available

The German portal Statista published an interesting

report in July 2018 about the number of films and

series available on Netflix per country (Statista,

2018). The top 4 countries in this ranking are, in

order, USA, Canada, UK and India. However, our

results show a shift in position between India and

Canada (Figure 1). Indeed, India has moved up to

second place and Canada to fourth. This change in

ranking has affected many countries such as

Australia, which gave way to Japan and France,

which took the place of Brazil...

Figure 1: Top 10 countries by number of issues available on

Netflix

Apart from the rankings, it can be seen that the

USA monopolizes about half of the programs

available on Netflix, followed by India with 16.8%

and the United Kingdom with 10.1%. Therefore,

more than three-quarters of all Netflix broadcasts are

available in three countries, and Netflix is available in

streaming in more than 190 countries (Help Center

Netflix, 2021).

Africa is also missing from the Top 10 countries

where Netflix content is available. Indeed, Egypt

comes in 12th position with 1.6% of content,

followed by Nigeria in the 20th position with a rate of

1.08%.

4.1.2 Ranking of Program Categories

Proposed by Netflix

The Netflix online video platform offers content in a

variety of genres. However, in our study, we were

interested in the categories offered, which can be

segmented into two main categories: movies and TV

shows.

Figure 2: Change in the number of issues offered by

category

We found that Movies account for 69% of the

content streamed on Netflix versus 31% on TV Show.

It is clear that Movies are the most popular and most

available content among the platform's subscribers.

Netﬂix Recommendation System based on TF-IDF and Cosine Similarity Algorithms

Nevertheless, if we analyze the trend of content being

put online over the years in figure 2, we can see that

the rate of evolution of TV shows continues to

increase, while from 2018, the number of movies

decreases, and for the first time, in 2020, the number

of TV shows broadcast exceeds the number of

movies. This observation also applies to the

beginning of 2021, with 19 new TV shows broadcast

compared to 12 movies.

This transition, which was identified in 2020,

raises questions to understand its causes. Indeed, one

of the possible causes may be due to the Covid 19

pandemic, which forced billions of people worldwide

to confine themselves to their homes for varying

periods depending on the country. These conditions

could probably influence the habits of users

concerning the monitoring of their television

programs.

Although we can see that the gap between the

number of movies and TV shows available on Netflix

has been decreasing more and moreover the last four

years, and it could well be that the transition that

marked the year 2020 is only a natural continuation

of the global pace that started a few years ago.

4.1.3 The Global Context of the Works

Offered on Netflix

A word cloud is a kind of semantic digest of a textual

corpus. It is the visual representation of the most

frequent words in a document. The more visible the

word is, the more frequent it is.

The word cloud is generally one of the first

visualization steps that allow building a global idea

about the subject matter of a textual corpus. It is a step

before implementing more advanced NLP

algorithms, which are likely to convey more precise

information about the semantics of the document.

Figure 3: Cloud of most frequent words in the program

description

igure 3 shows the most commonly used words

in the description of the works distributed by Netflix.

Words such as "Family", "World", "Life", and

"friend" are the most common, suggesting that most

online programs are dealing with social issues.

However, the frequency of a word is not a good

indicator of its relevance to the context. Indeed, the

principle of the frequency of a word is taken up by the

Bag-of-word model, which is a simple representation

of documents in the form of vectors calculated

according to the frequency of a word in the corpus. In

the NLP domain, this model is not sufficient to have

a more solid idea about the context of the document.

This is why techniques such as Word Embedding

(Ensaf, 2016), Word2vec (Mikolov, 2013) or TF-IDF

are more used in this sense.

4.2 Recommendation System based on

TF-IDF and Cosine Similarity

Netflix offers, from the platform's home page, the

classification of its movies and TV shows by genre.

In fact, Netflix values content according to the movies

viewed by users. However, the Netflix service has a

system of personalized recommendations to propose

to the subscriber the programs likely to interest him.

Netflix's recommendation system is based on several

criteria such as the customer's interaction with the

service, the choice of other users whose tastes are

deemed similar to those of the customer in question,

the metadata specific to the programs, the time of day

the user connects to the platform, the duration of

viewing time, etc. However, Netflix states that its

recommendation algorithm does not consider user

demographics such as age and gender (Help Center

Netflix, 2021).

We found it interesting to implement a

recommendation system that considers other metrics

such as the contextual similarity of the titles and

descriptions of the proposed works. First, we applied

the algorithm to all the programs available on the

platform regardless of their actual availability in the

geographical area where the client is located.

Table 1 illustrates the recommendation of 10

issues whose titles and descriptions are deemed to

have similarities with the "NCIS" series. These are

the implementation of the TF-IDF and Cosine

Similarity algorithms.

This recommendation system uses as a document

only the title and the description of the proposed issue

on Netflix. In other words, the application of the IF-

IDF and Cosine Similarity algorithms on such a small

corpus will certainly not give relevant results.

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Table 1: List of recommended emissions following the

selection of NCIS

Title Calculated Score

La Piloto 0.219489577073954

Twins Mission 0.185445602841902

Curon 0.165079412202837

Mugamoodi 0.161729302850993

Hashoter Hatov 0.160847112954766

Nagi-Asu: A Lull in the Sea 0.154977034987895

The Indian Detective 0.154310875273481

The Unremarkable Juanquini 0.142036012436844

Michael 0.141768479700181

Happyish 0.140348670951172

However, this approach is a starting point towards

a more sophisticated recommendation system that

considers other features such as the show’s duration,

the Netflix score, the leading players, etc. In addition,

the fact that Netflix does not take into account the

demographics of the subscribers led us to take this

feature into account as well to be able to increase the

probability of acceptance of the recommendations by

the client.

5 DISCUSSION AND

CONCLUSION

Netflix is the most popular on-demand broadcast

platform at the moment. It broadcasts thousands of

programs to subscribers in 190 countries around the

world. Since its inception, movies have been the most

dominant content in online programming. However,

the year 2020 marks the domination of TV shows for

the first time, which is also the case for the beginning

of the year 2021.

In our study, we conducted an exploratory

analysis of Netflix data. This analysis highlighted

information such as the countries where Netflix

content is most available, including the USA, India

and the United Kingdom, the distribution of programs

broadcast by category (69% movies and 31% TV

shows), and the semantic digest of the words used in

the descriptions of the works broadcast. On the other

hand, and due to the increase in the number of TV

shows offered at the expense of movies in 2020, we

thought it would be interesting to conduct a more in-

depth study on this subject to understand the causes.

Indeed, among the probable causes, we could cite the

COVID-19 pandemic, which has forced billions of

people worldwide to confine themselves to their

homes, which has probably impacted their TV

viewing habits.

The second part of this work consists of

implementing a system of program recommendation

by applying the TF-IDF and Cosine Similarity

algorithms on the titles and the descriptions of the

works. However, the relevance of the results of this

system can be criticized because of the limited

number of occurrences present in the used corpus.

Nevertheless, it can be an excellent start to

understanding other features that could refine the

recommendations, such as the show’s duration, the

score attributed on Netflix, the actors highlighted in

the program, etc. In addition, we thought it would be

interesting to include the demographic data of the

subscribers since Netflix does not consider this factor

when proposing recommendations.

Nevertheless, our work is characterized by the

simplicity and the low volume of training data

required to implement the recommendation system.

This gives it the advantage of being easily

implemented and used.

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