IRJET- An Intuitive Sky-High View of Recommendation Systems

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 02 | Feb 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1164
An Intuitive Sky-High View of Recommendation Systems
Rohit Sahoo1, Vedang Naik2
1,2Student, Department of Computer Engineering, TEC, University of Mumbai, Mumbai, India
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Abstract - Never in eons humanity has ever experienced
an explosion of information exchange like it does today with
the popularization of the Internet and the advent of social
media, e-commerce and other content. People face an ever-
increasing amount of options in almost all areas of human
life. This, in turn, leads to a paradox of choice. To solve this
dilemma the user turns to obtain recommendations from
people who have faced a similar choice or whose opinions
they value. But in this day and age, more and more users
rely on computational recommendation systems to help
with their decisions. It is suggested that the Netflix
recommendation system affects about 80% of the streaming
choices made by the consumer. With this in mind, it is
paramount for any industry that relies on user engagement
to develop effective recommendation systems [1].
Key Words: Recommendation Systems, Content-Based,
Collaborative Filtering, Model-based Filtering,
Memory-Based Filtering, User-Based Collaborative
Filtering, Item-Based Collaborative Filtering
1. INTRODUCTION
The process of making recommendations generally starts
with the user providing his/her preferences to the
recommendation system. The recommendation system
then, based on these preferences provides
recommendations when asked by the user for the same. It
is also possible for the system to obtain preferences from
the rest of the user’s and initially recommend the baseline
of those preferences to our current user.
Fig -1: Recommendation Process
2. RECOMMENDATION SYSTEMS
Recommendation Systems are software tools and
techniques providing suggestions for items to be of use to a
user. “Item” is the general term used to denote what the
system recommends to users. A recommendation system
predicts the probability of a user liking a particular item
and recommends the user items that the user is more
inclined to prefer. To make these predictions the
recommendation system uses the data obtained from the
user interacting with the system. This data is represented
as a Utility matrix that contains the users in the rows and
the items in the columns. The preferences or lack thereof
are represented by numbers [2].
Fig -2: Recommendation System
Data about each user’s rating for each item is seldom
available making most utility matrices sparsely populated.
This is known as the sparsity problem.
Fig -3: Utility Matrix
Recommendation systems can be broadly classified into
two types:

2.1 Content-Based Recommendation System
Fig -4: Content-Based Recommendation System
The content-based recommendation systems rely
on the items consumed by the user. For example, in a
content-based book recommendation system, someone
who likes Dune by Frank Herbert can be recommended
Frankenstein by Mary Shelley since both books are based
on science-fiction. The content-based system works on the
creation of a profile which can be described as a list of
salient characteristics of the item and a user profile that
summarizes the preferences of the user based on those
characteristics.
2.2 Collaborative-Filtering
Collaborative-Filtering is based on characterizing users and
items based on their previous interactions [3]. There are
mainly two types of collaborative filtering:
2.2.1 Memory-Based
This approach uses the entire user-item database to
generate a prediction. It uses statistical techniques to find
users that share similar interests as that of the user and
have rated different items similarly [4]. The similarity is
often calculated by using Pearson Correlation:
∑ ( ̅ ) ( ̅ )
√∑ ̅ ∑ ( ̅ )
Where is the rating given to item i by the user a; is
the mean rating given by users; and m is the total number
of items. The predictions are calculated based on
̅
∑ ( ̅ )
∑
Where is the prediction for the active user a for item i;
is the similarity between users a and u; and n is the
number of users in the neighborhood. The model then
recommends the top-N recommendations using a variety of
algorithms to combine the preferences of the user’s
neighbors [3].
2.2.2 Model-Based
In this approach, instead of working with a sparsely
populated dataset the algorithms first predict the value of a
user’s rating for a particular unrated item based on their
previous ratings for similar items.
There are multiple machine learning algorithms such as
Bayesian network, clustering, and rule-based approaches
[4].
2.2.3 Collaborative-Filtering Methods
There are two main methods for implementing
collaborative filtering.
Fig -5: Collaborative Filtering Methods
2.2.3(a) User-Based
Fig -6: User-Based Collaborative Filtering
This method is based on the principle that users who
share the same preferences will like similar things. We
first find a set of users that have interests similar to our
current user. We then use their utility matrices to fill in the
blanks in our current user’s utility matrix [5].

2.2.3(b) Item-Based
Fig -7: Item-Based Collaborative Filtering
This method focuses on the items instead of the users
where the items that are similar to the current user’s
purchased products are recommended to the user. It
works on the principle that the user is more likely to buy
something similar to what they have already purchased
before. This method provides more reliable results
because it has been observed that it is easier to find items
with similar characteristics than it is to find users that
have completely similar interests [5].
3. SUMMARY
Content-based recommendation systems are advisable
where the items can be clustered together and the user's
picks don't vary greatly from the choices, they already
have a predilection to make. Whereas Collaborative-
Filtering should be preferred when the consumer's
preferences are based more on their personality instead of
the current item that they are accessing. The prevalence of
recommendation systems is unquestioned in our day to
day choices, thus for any product/customer-oriented
enterprise, it is of paramount importance to have an
appropriate recommendation system setup to guide the
consumers in their decision-making process.
REFERENCES
[1] C. A. Gomez-Uribe and N. Hunt, “The Netflix
Recommender System,” ACM Transactions on
Management Information Systems, vol. 6(4), pp. 13:1-
13:19, Dec. 2015.
[2] F. Ricci, L. Rokach and B. Shapira, "Recommender
Systems: Introduction and Challenges," in
Recommender Systems Handbook, Springer US, pp. 1-
34, 2015.
[3] Z. Huang, D. Zeng and H. Chen, "A Comparison of
Collaborative-Filtering Recommendation Algorithms
for E-commerce," IEEE Intelligent Systems, vol. 22, pp.
68-78, Sept.-Oct. 2007.
[4] B. Sarwar, G. Karypis, J. Konstan and J. Riedl, Item-
Based Collaborative Filtering Recommendation
Algorithms: 10th International Conference On World
Wide Web, May 01-05, 2001, Hong Kong, Hong Kong,
2001.
[5] J. Leskovec, A. Rajaraman and J. Ullman,
"Recommendation Systems," in Mining of Massive
Datasets, Cambridge University Press, pp. 292-323,
Nov. 2014.

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