recommendation system

1. Shri Balaji Institute of Technology
and Management
Content Based Recommendation System with
sentiment analysis
Department Of Computer Science & Engineering
Session 2022-23
Major Project
On
Guided by
Prof Satish Chadokar
Presented by
Naman Jain - 0545CS191027
Ayush Thakre - 0545CS191011

2. Table Of Content
1) Abstract 2) Introduction
3) Objective 4) Tools and
Technologies
5) Use - Case Diagram 6) Existing System
7) Proposed System 8) Conclusion
9) Future Scope 10) Reference

3. Abstract
Recommendation systems attempt to predict the
preference or rating that a user would give to an item.
Knowledge discovery techniques can be applied to the
problem of making personalized recommendations about
items or information during a user's visit to a website.
Collaborative Filtering algorithms give recommendations
to a user based on the ratings of other users in the
system. Traditional collaborative filtering algorithms face
issues such as scalability, sparsity and cold start. In the
proposed framework, prediction using item based
collaborative filtering is combined with prediction using
demographics based user clusters in an adaptive
weighted scheme. The proposed solution will be scalable
while addressing user cold start

4. Introduction
During the last few decades, with the rise of Youtube, Amazon,
Netflix and many other such web services, recommender systems
have taken more and more place in our lives. From e-commerce
(suggest to buyers articles that could interest them) to online
advertisement (suggest to users the right contents, matching their
preferences), recommender systems are today unavoidable in our
daily online journeys.In a very general way, recommender systems
are algorithms aimed at suggesting relevant items to users (items
being movies to watch, text to read, products to buy or anything else
depending on industries).Recommender systems are really critical in
some Industries.

6. Objective
The objective of recommender systems is to
provide recommendations based on recorded
information on the users' preferences. These
systems use information filtering techniques to
process information and provide the user with
potentially more relevant items.
Recommendation system provides the facility to
understand a person's taste and find new,
desirable content for them automatically based on
the pattern between their likes and rating of
different items.

7. Existing System
In earlier times there was no Recommender systems (or advance
Recommender systems).this was the big problem for the user to
search there desired things again and again.
Same as they problem for they company and software developer to
know what user want so.
Visual search could not work without Recommender systems(Visual
search:-Visual search is exactly what the name implies: customers
search for products using images).

8. Proposed System
We propose a recommendation system for the large amount data
available on the web in the form of ratings,reviews, opinions, complain,
remarks, feedback, and comments about any item (product, event,
individual and services). Here we recommended a hybrid filtering
technique to filter different types of reviews, opinions,
remarks,comments, complains etc. Because recommendations are
based on ratings, ranks, content, reviewer’s behavior, and timing of
review generated by different reviewers. We study a recommendation
based on numerical data like Ratings or rank provided for different
product or services. Recommendation by applying the weightage of
summarized reviews and opinions on the rating of item are proposing as
future work.

9. Recommendation System Working

10. Front End Languages
The HyperText Markup Language or HTML is the standard markup language for documents designed to be
displayed in a web browser. It can be assisted by technologies such as Cascading Style Sheets and
scripting languages such as JavaScript Cascading Style Sheets is a style sheet language used for describing
the presentation of a document written in a markup language such as HTML or XML. CSS is a cornerstone
technology of the World Wide Web, alongside HTML and JavaScript.

11. Tools and Technologies
FLASK -Flask is a micro web framework written in Python. It is classified as a microframework because it
does not require particular tools or libraries. It has no database abstraction layer, form validation, or any
other components where pre-existing third-party libraries provide common functions.
JavaScript- Javascript (JS) is a scripting languages, primarily used on the Web. It is used to
enhance HTML pages and is commonly found embedded in HTML code.

12. Tools and Technologies
Python is a high-level, general-purpose
programming language. Its design philosophy
emphasizes code readability with the use of
significant indentation. Python is dynamically-
typed and garbage-collected. It supports
multiple programming paradigms, including
structured, object-oriented and functional
programming.

13. Recommendation System Use-Case Diagram

14. Cosine Similarity ?
Cosine similarity is a metric used to measure
how similar the documents are irrespective of
their size. Mathematically, it measures the
cosine of the angle between two vectors
projected in a multi-dimensional space. The
cosine similarity is advantageous because
even if the two similar documents are far apart
by the Euclidean distance (due to the size of
the document), chances are they may still be
oriented closer together. The smaller the
angle, higher the cosine similarity.

15. Sentiment Analysis ?
Sentiment analysis, also referred to as opinion
mining, is an approach to natural language
processing (NLP) that identifies the emotional
tone behind a body of text. This is a popular
way for organizations to determine and
categorize opinions about a product, service, or
idea.
Sentiment analysis studies the subjective
information in an expression, that is, the
opinions, appraisals, emotions, or attitudes
towards a topic, person or entity. Expressions
can be classified as positive, negative, or
neutral. For example:”I really like your ppt.” →
Positive.

16. Conclusion
Data in the form of reviews, opinions, feedback,
remarks, and complaint treated as Big Data cannot be
used directly for recommendation system. These data
first filter/transform as per requirement. we discussed
filtering techniques and issues related for handling text
data. We have implemented recommendation system for
movie lan dataset, on analyzed with different size files.
Resultant graph is showing that whenever file size is
increasing the execution time is not increasing in the
same ratio and we know that data size that are in the
form of ratings, ranks, review,feedback are increasing
drastically. Here we are proposing Recommendation by
applying the weightage of summarized reviews and
opinions on the rating of item as future enhancement in
this work.

17. Future Scope
● Presents the movie which has not become very famous in front of the
people.
● Recommender systems can be a very powerful tool in a company’s
arsenal, and future developments are going to increase business value
even further.
● These systems use information filtering techniques to process
information and provide the user with potentially more relevant items.
● In the future, we could build a hybrid model that takes all the
demographics, content as well as the user preferences into accounts
before giving out the recommendations.

18. Reference
1.Systematic analysis of Movie Recommendation System through Sentiment Analysis R Lavanya1 , B. Bharathi2 1
Research Scholar, Department of Computer Science and Engineering, Sathyabama Institute of Science and
Technology, Chennai 2 Professor, Department of Computer Science and Engineering, Sathyabama Institute of
Science and Technology, Chennai lavanya27382@gmail.com1
2.Movie Recommendation System Using Sentiment Analysis From Microblogging Data Sudhanshu Kumar , Kanjar
De, and Partha Pratim Roy
3.Methods and Tools for Building Recommender Systems Yuri Stekh1 , Mykhoylo Lobur2 , Vitalij Artsibasov3 ,
Vitalij Chystyak4
4.F. Abel, Q. Gao, G.-J. Houben, and K. Tao, “Analyzing user modeling on Twitter for personalized news
recommendations,” in Proc. 19th Int. Conf. Modeling, Adaption, Pers. (UMAP). Berlin, Germany: SpringerVerlag,
2011, pp. 1–1
5. R. Burke, “Hybrid recommender systems: Survey and experiments,” User Model. User-Adapted Interact., vol.
12, no. 4, pp. 331–370, 2002.