An Analytical Study and Analysis of Filtering Techniques used for Classification of Recommender Systems

Abstract

*in partial fulfilment for the award of the degree of Bachelor of Computer Applications*

This research proposes a classification of recommender systems based upon the filtering techniques used for refining the recommendations. The aim is to explore different types of filtering techniques and ideas and use them to classify the recommender systems with justifications, their advantages, and disadvantages, and then compare these techniques on the basis of input collection, processing data, and output as a recommendation. The classification is on the basis of how a system treats the data, the collection process of the data, approaches of collecting and processing the data, and the area of application.
In the research, we have classified the recommender systems using various filtering techniques, viz. collaborative filtering, content-based filtering, demographic filtering, and knowledge-based filtering techniques of data processing, approaches, and applications. These techniques have been diagrammatically represented throughout the chapters to best explain the scenarios where these techniques can be helpful. The research also discusses the shortfalls of these techniques in their respective region and where and how they overcome the limitations of other techniques described throughout the research.

Full text

An Analytical Study and Analysis of Filtering Techniques
used for Classification of Recommender Systems
DISSERTATION REPORT
Submitted by
Mohammad Muzammil Khan
2017 – 301 – 080
in partial fulfilment for the award of the degree of
Bachelor of Computer Applications
Under the supervision of
Prof. M. Afshar Alam
Department of Computer Science & Engineering
School of Engineering Sciences & Technology
JAMIA HAMDARD
(Deemed to be University)
New Delhi-110062
2020

Table of Contents
CERTIFICATE ............................................................................................... I
DECLARATION ........................................................................................... II
ACKNOWLEDGEMENT .......................................................................... III
List of Abbreviation(s) ................................................................................ IV
List of Table(s) ............................................................................................... V
List of Figure(s) ............................................................................................ VI
Title and Abstract ........................................................................................... 1
Chapter 1: Introduction ................................................................................. 3
Chapter 2: Past researches ............................................................................ 8
Chapter 3: Basis of Classification of Recommender Systems .................. 12
3.1 Definition ............................................................................................... 13
3.2 Scope ...................................................................................................... 14
3.3 Basis of Classification ........................................................................... 14
Chapter 4: Collaborative Filtering ............................................................. 16
4.1 User-based and Item-based Collaborative Filtering techniques .......... 20
4.2 Model-Based Collaborative Filtering techniques ................................. 22
Chapter 5: Content-based Filtering ............................................................ 24
5.1 Heuristic-Based ..................................................................................... 27
5.2 Model-Based .......................................................................................... 28
5.3 Web mining-Based ................................................................................. 28
Chapter 6: Demographic Filtering .............................................................. 30
Chapter 7: Knowledge-based Filtering ...................................................... 35
7.1 Case-based recommendations ............................................................... 40
7.2 Constraint-based recommendations ...................................................... 41
Chapter 8: Comparision of Classified Recommender Systems ............... 42
Chapter 9: Conclusion .................................................................................. 47
References ...................................................................................................... 50
Conference certificate and conference paper ............................................... 54

I
Certificate

II
DECLARATION
I, Mohammad Muzammil Khan, a student of Bachelor of
Computer Applications (BCA) (Enrolment No: 2017-301-080) hereby
declare that the dissertation entitled “An Analytical Study and Analysis of
Filtering Techniques used for Classification of Recommender Systems”
which is being submitted by me to the Department of Computer Science &
Engineering, School of Engineering Sciences & Technology, Jamia
Hamdard, New Delhi in partial fulfilment of the requirement for the award
of the degree of Bachelor of Computer Applications (BCA), is my
original work and has not been submitted anywhere else for the award of
any Degree, Diploma, Associateship, Fellowship or other similar title or
recognition.
Mohammad Muzammil Khan
Date:
Place: New Delhi, India

III
ACKNOWLEDGEMENT
“And remember! Your Lord caused to be declared: If ye are grateful, I will add more unto you; …”
(The Quran, Ibrahim:7)
All thanks to Allah, the Almighty who graced me the opportunity and
stamina to complete this project with ease and well within time.
After the Almighty, I would like to extend my gratitude to my supervisor
and mentor Prof. M. Afshar Alam, Dean of School of Engineering Sciences
and Technology, Head of Department of Computer Science and
Engineering, Jamia Hamdard, who was extremely helpful and provided me
with much-needed support in all my highs and lows. I am in awe of his
dedication and deeply revere his great personality. His dynamism, vision,
sincerity, and motivation have greatly inspired me. I am greatly privileged
and honoured to study under his esteemed guidance.
I am extremely grateful to my family for their love, care, prayers, and
sacrifices for educating and helping me overcome all the obstacles of my
life. Especially my sister, Ms Darakhshan Ishrat, who helped me in ways I
can’t even begin to fathom.
I must thank all of my teachers for showing me, each in their unique way,
what it means to be dedicated to your work. All of them have given me their
time, energy, and expertise, and hence, made me richer for it. I shall always
be indebted to them. My completion of this work would not have been
possible without the kind support of my teachers who either directly or
indirectly shaped this work.
Mohammad Muzammil Khan

IV
List of Abbreviation(s)
S. No.
Abbreviation
Full form
1
RS/RecSys
Recommender System(s)
2
CF
Collaborative Filtering
3
k-NN
k-nearest neighbour
4
MBCF
Model-Based Collaborative Filtering
5
CBF
Content-based Filtering
6
DF
Demographic Filtering
7
KBF
Knowledge-based Filtering
8
HRS
Hybrid Recommender System

V
List of Table(s)
S. No.
Table No.
Caption
Page no.
1
Table 4.1
Comparison on the basis of input,
processing, and output.
44

VI
List of Figure(s)
S. No.
Figure No.
Caption
Page no.
1
Figure 4.1
Representation of Collaborative
Filtering
17
2
Figure 4.2
A recommendation is made based on CF
18
3
Figure 5.1
Representation of Content-based
Filtering
25
4
Figure 5.2
Item is recommended based on CBF
26
5
Figure 6.1
Representation of Demographic
Filtering
32
6
Figure 6.2
Recommendation for demographic
neighbourhood
33
7
Figure 7.1
A query to a recommender system
36
8
Figure 7.2
Recommendation based on KBF system
38

1
Title and Abstract
An Analytical Study and Analysis of Filtering Techniques Used
for Classification of Recommender Systems
1
Abstract. This research proposes a classification of recommender
systems based upon the filtering techniques used for refining the
recommendations. The aim is to explore different types of filtering
techniques and ideas and use them to classify the recommender systems
with justifications, their advantages, and disadvantages, and then compare
these techniques on the basis of input collection, processing data, and output
1
Khan, Mohammad Muzammil, A Review on Filtering Techniques Used for Classification of Recommender
Systems (March 29, 2020). Available at
SSRN: https://ssrn.com/abstract=3563557 or http://dx.doi.org/10.2139/ssrn.3563557
Presented at the 3rd International Conference on Innovative Computing & Communications (ICICC) – 2020
Held on 21 – 23 February, 2020.

2
as a recommendation. The classification is on the basis of how a system
treats the data, the collection process of the data, approaches of collecting
and processing the data, and the area of application.
In the research, we have classified the recommender systems using
various filtering techniques, viz. collaborative filtering, content-based
filtering, demographic filtering, and knowledge-based filtering techniques
of data processing, approaches, and applications. These techniques have
been diagrammatically represented throughout the chapters to best explain
the scenarios where these techniques can be helpful. The research also
discusses the shortfalls of these techniques in their respective region and
where and how they overcome the limitations of other techniques described
throughout the research.
Keywords. Recommender Systems, demographic filtering, collaborative
filtering, content-based filtering, knowledge-based filtering.

3
CHAPTER I
INTRODUCTION

4
Introduction
With time, the technological advancements have led to buyers facing
difficulties instead of ease in finding an equilibrium between their
needs/wants and the products available to them. Because of the
advancements, the consumer class has a wide variety of choices and
collection, be it beneficial or not, available now than ever before. This is
where the Recommender Systems play a huge role by understanding the
behaviour of a consumer, purchase analysis, prediction of purchase, etc.
therefore, helping buyers get what they would prefer.
Today, we use Recommendation Systems (RecSys) everywhere; whether
it is online shopping for items [1], streaming services for audio and videos
or a recommendation for doctors and hospitals, etc. [2, 3]. We are
surrounded by systems that determine what we would like and recommend
it to us. The fact is that we are so accustomed to the recommendations that
the online world we recognize today is very much built by automated
systems that determine our likes for us. If not for these systems, the online
world would not be recognizable as we are used to today.

5
Many research papers have been reported, especially in the field of book
recommendation [4, 5], and several techniques have been used including
novel fuzzy techniques [6], Ordered Weighted Averaging [2], and implicit
feedback for recommendations [7 – 9].
The research in recommendation technology has always longed for new
and innovative approaches. A system that is relevant today might not be able
to yield good recommendations in the next, say, five years. The technology
has been continuously improving but the true potential of recommender
systems remains somewhat untapped. There is still much yet to be
discovered which could help in improving the recommendations and user
experiences. With the help of this research, we aim to classify and justify
the classification of these recommender systems.
Recommender systems are very crucial in some industries since they can
produce a huge amount of profit if they are efficient or can also be a way to
stand out significantly from contenders. As a proof of the importance of the
recommender systems, we can mention that, from 2006 to 2009, Netflix, a
video streaming service, organized a campaign called “the Netflix
Prize” aimed at producing a recommender system that performs better than
its current algorithm with a prize of US$1 million. BellKor's Pragmatic
Chaos team managed to win this contest in 2009 [10] by creating a system
which was better than what Netflix had and produced recommendations
10% more accurately.
Different types of recommender systems face different difficulties in the
establishment phase as well as later stages where the network gets too big,
as we shall discuss in detail in chapter four. Some systems overcome other

6
system’s complications but introduce another problem in the process. One
of the main problems faced with every recommender system is the problem
of a cold start. A cold start is when the system doesn’t have enough
information, either about the user or the consumer, to accurately predict
what the user/consumer would like to purchase or have. The problem leads
to having a nonsensical recommendation for the consumer in case of a lack
of information about the user/consumer or not having recommended the best
possible recommendation because the system didn’t have information about
the product. The cold start problem can deter, dissuade and deprive the new
coming consumer. Therefore, a dearth of much-needed exposure of newly
added products takes place. We shall discuss how to deal with this problem
efficiently, including other problems, with solutions and methods to use
throughout the research.
Therefore, it is imperative to have an understanding of the different types
of recommender systems, their usage, fields, etc. to effectively use the best
system in a given environment. This paper will describe and classify the
fundamental filtering techniques used in Recommender Systems, classify
the systems based on these techniques, and how they differ on the basis of
techniques used to collect data, process the data, and infer recommendation
based on previously known attributes or newly learnt information about
either the user or product as well as discuss the possible solutions to
problems faced by a recommender system in detail.
The report is structured as follows - Chapter Two will give a review of
the past researches done in the field of recommendation technology and
their innovative takes. Then we will move on to Chapter Three which will
provide a window into the basis of classification and scope of this research.

7
Then from Chapter Four to Chapter Seven, we will discuss classified
filtering techniques of recommender systems, each having a different
perspective at the technique of filtering for our classification, enriched with
examples and different scenarios to help visualize the recommendation
process better. Then in Chapter Eight, we shall compare these techniques on
the basis of data collection, processing, and output as a recommendation.
Finally, in Chapter Nine, we shall conclude the research with our findings
and discuss our approach for classification.