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the ample amount of data being present on the web,it became very important to
use filtering techniques to find our interest of information. In this paper, we
introduce various filtering techniques to ease our work of finding relevant
information.Content based and collaborative techniques are the most widely used
and are discussed in the paper with their pros and cons. However, the newest
research in this area- Hybrid recommendations are also included in this paper.

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With the immense increase in the amount of information available,
retrieving information of our interest has became very difficult. With the
development of Information Retrieval systems such as Google, Alta Vista, the
task of retrieving information has become quite easy but upto some extent.

The Recommendation systems have fully solves this problem. It has now
become the most powerful tool in electronic commerce. In recent years, sRecommendations systems have become immensely popular
and are used in variety of areas such as in movies, in dating websites, in
restaurants, in social tags and in news.

 Recommendation systems are
softwares that provide suggestions for items to be of use to the user.The
suggestions can be related to various decision-making processes such as which
item to buy, which news to read , which movies to watch , which music to
listen. Over the years, various approaches of recommender systems have been
created – collaborative ,content-based
filtering.However  the newest technology
“hybrid recommendations-mixture of collaborative and content’ also comes into
picture now a days.

3. Phases of Recommendation

3.1 Loading and formatting data

Dataset is a collection of interests and likes of
various users by using which we can recommends products and items to
them..Dataset can be downloaded from various websites such as movielens,frappe, CoMoDa Dataset.

3.1.1 Data collections are done by two methods-explicitly and

data  include the following:

Asking a customer to rate an object.

Asking a customer to rank a collection of object
from most favourite to least favourite.

Presenting two object to a customer and
asking to choose the better one of them.

Asking a customer to create a list of object
that they likes.


data collection includes the following:

Keep observing the object that a customer
views in an online store.

Analyzing its viewing time.

Keeping a track of the object that a customer
purchases online.


3.1.2 Datasets are normally in the form 1 :

{ critic,title,rating}

where critic is the user,

title is the item it rated,

rating is the rating given by the user.


The next step is to arrange the data in a
format that is useful to build the recommendation engine .The current data
contains a row containing critic,title and rating.This has to be converted to
matrix format containing critics as rows,title as columns, and ratings as the
cell values.

The data can be viewed as :


User 1

User 2

User 3

User 4

Item 1





Item 2





Item 3







3.2 Calculating similarity
between the users

This is the very important step as we have to find the
similarity between two users to recommend items to them. Various similarity
measures are available 2.


often want to compare two feature vectors, to measure how similar they are. We
hope that similar patterns will behave in a similar way.

Hamming distance

Hamming distance

Similarity using correlation

distance is not well normalised .So, we use correlations.


Pearson Correlation Coefficient

Recommending items to users

The final step is to recommend items to the users.


4 .Recommendation filtering

A recommendation system must predict an item that is
worth recommending.In order to do this, a system must able to predict the
utility of some of items, and then decide what items to recommend based on
their comparison 3.

4.1 Content based filtering

This is the most famous filtering and is used widely .Content
based recommendation is targeted at personal level and considers individual
preferences and contents of the products for generating recommendations.

Basically these content based recommendations system
elaborate a specific profile of each content and then  perform some correlation matrices on
data.Various items are compared with items previously rated by the user and
best matching items are recommended. Collaborative algorithm does two main
tasks :

a user profile that describes the types of items the user likes/dislikes

the user profile to some reference characteristics (with the aim to predict
whether the user is interested in an unseen item)


Various advantages of content based
filtering are :

(a)Content based systems are user dependent.

(b) These systems are highly transparent.

there are various challenges of content based filtering :

(a)    Lack of diversity : No
suitable suggestions if the analyzed content does not contain enough
information to discriminate items the user likes from items the user does not

(b)   Scalability : Content based
filtering is not scalable.

4.2 Collaborative filtering Recommendations

Another filtering technology that is widely used in
recommender systems is Collaborative Filtering.As compared to content based
filtering ,collaborative recommender system can automatically filter the
information that the content based system could not represent and gives up to
date recommendations and informations 4.

Collaborative filtering was developed in late 1990’s
and it is the most famous filtering technique till date .Various online web
services such as Amazon and Netfix are utilizing this filtering techniques.

Collaborative filtering algorithms usually separated
into two parts: 

Model-based algorithm.

Memory-based algorithm.


4.2.1 Memory-based Collaborative

based collaborative filtering store  the
entire customer ratings into their memory.

are also called lazy recommendation algorithms. They do not immediately attempt
to calculate customer precedence of an object. Typical examples of this approach are neighbourhood-based CF
and item-based/user-based top-N recommendations.

types of memory based CF are there:

Item/Object-based filtering.

User/Customer-based filtering.


filtering :  Calculate similarity between items and
make recommendations. Items usually don’t change much, so this often can be
computed off line. Item/Object-based filtering was recommended by Sarwar et al at 2001 5. Items/objects are
compared for similarity. The neighborhood of most likely objects is recognized,
for every object that belongs to the customer who is active .

Based Algorithm6

For every object “o” that “c” has preference
for yet

For every object “p” that “c” has a preference

Compute a similarity s between “o” and “p”

Add “c”‘s preference for “p”, weighted by s,
to a running average

Return the top objects , ranked by weighted



filtering: Recommend
items by finding similar users. This is often harder to scale because of the
dynamic nature of users.

based Algorithm6

For every object “o” that “c” has no
preference for yet

For every other customer “d” that has a
preference for “o”

Compute a similarity s between “c” and “d”

Add “d”‘s preference for “o”, weighted by s,
to a running average

Return the top objects, ranked by weighted


4.2.2 Model-based
Collaborative Filtering: memory based  tackle
the task of “guessing” how much a user will like an item that they did not
encounter before. For that they utilize several machine learning algorithms to
train on the vector of items for a specific user, then they can build a model
that can predict the user’s rating for a new item that has
just been added to the system.

Popular model-based techniques are Bayesian Networks,
Singular Value Decomposition, and Probabilistic Latent Semantic Analysis7.



Scalability4: There are millions of users and items
present.Thus a large amount of computation power is important to calculate
recommendation. For example, with millions of customers (C) and millions of
distinct items (O), a CF algorithm with the complexity of O(n) is already too
large. Also, many systems need to react immediately to online requirements and
make recommendations for all users regardless of their purchases and ratings
history, which demands a high scalability of a CF system.

Data sparsity8 : If a customer or user  has evaluated very few items then its quite
difficult to know his taste and his preferences and in this case he could be
related to wrong neighbourhood8.So, this lack of information is quite

Cold start problem9 :  This is a
situation where a recommendor system do not have adequate information about a
customer or object in order to make relevant predictions.


4.3 Hybrid recommendations system

Hybrid systems are the newest recommendor system and
combines the best feature of both content based and collaborative based
filtering .

Types of hybrid systems are :

(a)    Weighted hybridisation9 : This technique combine
the results of collaborative and content based filtering to generate a
prediction by integrating the techniques of both the recommendations.

(b)   Switching hybridisation10 : This technique switches
to other recommendations techniques based on the needs of the moment.It solves
the new user problem .

(c)    Mixed hybridisation11 :Instead of having only one
recommendation per object, this technique combine results of different
recommendations techniques at the same time.

(d)   Cascade Hybridisation 12: Refinement is done in
cascade hybridisation. Results from one recommendations acts as input to other
recommendation techniques.




5.Conclusion and Future Work

This paper presented the various techniques and
algorithm to build the recommender system. We study various research paper and realised
that Collaborative filtering is the mostly used filtering technique but there
are various problems related to this such as data sparsity,cold start problem ,scalability.
We have also introduced various advantages and disadvantages related to these
techniques. Various areas where still much research is to be done in coming
years has also been discussed (hybrid recommendations).



1. Building recommendation engines by By Suresh Kumar

2.Similarity and recommender systems by Hiroshi

3. Introduction to Recommender Systems Handbook by Francesco
Ricci, Lior Rokach and

Bracha Shapira

 4. A Survey on
Recommender Systems based on Collaborative Filtering Technique  by Atisha sachan.

5.Sarwar.B, Karypis.G, Konstan.J, and Riedl.J. (2001).
“Item-Based Collaborative Filtering Recommendation Algorithms”. In WWW
’01: Proceedings of the 10th International Conference on World Wide Web, New
York, NY, USA.11.

6. Item based and user based recommendation difference
in Mahout by data science

7.Memory-Based vs. Model-Based Recommendation Systems
by yasserebrahim

8.Survey Paper on 
Recommendation System  by  Mukta kohar,Chhavi Rana

9.Recommendation systems : principles,methods and
evaluations  by F.O.Isinkaye


10. An Improved Switching Hybrid Recommender System
Using Naive Bayes Classi?er and Collaborative Filtering by Mustansar Ali Ghazanfar
and Adam Pru¨gel-Bennett ?

11. A mixed hybrid recommender system for given names
by Rafael Glauber1, Angelo Loula1, and Jo˜ao B. Rocha-Junior2

12.A Hybrid Recommender System Using Link Analysis and
Genetic Tuning in the Bipartite Network of by Brett Boge

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