SMART ARENA

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COLLEGE OF ENGINEERING, CHENGANNUR

KERALA

DEPARTMENT OF COMPUTER ENGINEERING

Certificate

This is to certify that the Main Project Report entitled

SMART ARENA

Submitted by

APARNA J

MRIDHULA KOONATH

RITA ANNIE THOMAS

STEPHEN REJI

SWATHY MOHAN

Under the guidance of

Mrs. Renu George

is a bonafide record of the work done by them.

Project Guide Co-ordinator Head of Department


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ACKNOWLEDGEMENT

I am greatly indebted to GOD, THE ALMIGHTY for showering his abundant bless-

ings upon us for the fulfillment of this project.

We express our deep sense of gratitude to Prof.Dr. Jyothiraj V.P, Principal,

College of Engineering, Chengannur, for providing the necessary facilities and help from

the management side to make this project successful.

We express our sincere thanks to Dr. Smitha Dharan, Head of the department,

Department of Computer Engineering for the insight she has given us which has resulted

in the successful completion of our project.

Our special thanks to Mrs. Manjusha S.Nair, Assistant Professor, Department

of Computer Engineering, and Project Coordinator for guiding us in our work and

providing timely advice and valuable suggestions.

We extend our sincere thanks to our Project Guide Mrs. Renu George , Assistant

Professor, Department of Computer Engineering for providing constant support and

encouragement.

We also thank all the teaching staff of College of Engineering, Chengannur, espe-

cially Computer Engineering Department. They have always been steady reflection of

dedication and hard work.

This humble endeavor wouldnt have become a success without the constant support,

inspiration and blessings from our parents. We express our love and gratitude to all our

well-wishers and dear one without whose support and encouragement this work would

have never been possible.


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ABSTRACT

Personalized service is an important trend in the development of information process-

ing technology. With the continuous rapid development and improvement of Internet

technology, there has been an explosive growth of information on the Internet. Person-alized service provides an automatic function that recommends items by obtaining and

analyzing user information; predictions based on the analysis and information are made

prior to the user launching a search. The core value of personalized services lies in its

recommendation capability.

SMART ARENA is a personalised recommendation system wherein which users can

watch, rate and search movies and according to the rating, the recommendations are

provided. It is also based on the user preferences and item features. Users preferencesfor different item features were obtained by employing user evaluations of the items. It is

expected that providing better recommendations according to preferences and features

would improve the accuracy and efficiency of recommendations and also make it easier

to deal with the data sparsity.


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Contents

1 INTRODUCTION 1

2 LITERATURE SURVEY 3

2.1 Existing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2 Proposed System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3 SOFTWARE REQUIREMENT SPECIFICATION 7

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.1.1 Purpose of Pro ject . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.1.2 Scope of the Pro ject . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.1.3 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.2 Overall Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.2.1 Product Perspective . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.2.2 Product Functions . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.3 External Interface Requirements . . . . . . . . . . . . . . . . . . . . . . . 9

3.3.1 User Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.3.2 Software Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.4 Other Non-Functional Requirements . . . . . . . . . . . . . . . . . . . . 10

3.4.1 Security Requirements . . . . . . . . . . . . . . . . . . . . . . . . 10

4 SYSTEM DESIGN 11

4.1 Input Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

4.2 Output Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

4.3 Data Flow Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4.4 Use-Case Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.5 ER Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4.6 Database Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

5 IMPLEMENTATION 19

5.1 Preference-Feature Model Development . . . . . . . . . . . . . . . . . . . 20

5.2 Prediction of user rating . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

5.3 accuWeigh algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

6 SCREENSHOTS 25

7 CONCLUSION and FUTURE SCOPE 39

8 REFERENCES 40

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List of Figures

1 jinni.com . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 movielens.org . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

3 criticker.com . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 imdb.com . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

5 DFD LEVEL 0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

6 DFD LEVEL 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

7 DFD LEVEL 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

8 Use Case Diagram for the System . . . . . . . . . . . . . . . . . . . . . . 14

9 ER DIAGRAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

10 Home page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

11 Login page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

12 Admin page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

13 Add Movie page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

14 Add Movie page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

15 Sign up page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

16 User profile and Recommendations . . . . . . . . . . . . . . . . . . . . . 31

17 Search Movie page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

18 Search results page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

19 Search by genre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

20 Search by language,director,year and IMDB rate . . . . . . . . . . . . . . 35

21 Search by genre results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

22 Watch and Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

23 Watch Movie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

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SMART ARENA BTECH MAIN PROJECT

1 INTRODUCTION

It is often necessary to make choices without sufficient personal experiences of the

alternatives. In everyday life, we rely on recommendations from other people either by

word of mouth, recommendation letters, movie and book reviews printed in newspapers,or general surveys. Recommender systems assist and augment this natural social pro-

cess. With increase demand of personalized services in e-commerce, recommendation

systems are playing a critical role in commercial websites. SMART ARENA is a recom-

mendation system that can recommend movies to the users. The accuWeigh algorithm

which we developed is based on user preference and item feature similarity is used in the

implementation of the system and is found to be more accurate than the conventional

algorithms. At present there are three mainstream recommendation algorithms:

The first type is the personalized time series algorithm. This type of algorithm de-

velops a time series relative to user search behaviour and then analyzes potential user

needs to provide recommendations. Obviously, this method is employed by adminis-

trators rather than users. The administrator only has to embed the most appropriate

series at the very beginning of a search to make it work. However, personalized time

series algorithms lack accuracy and do not meet different users demands.

The second type is the user-item association based algorithm. This method gener-

ates matching pairs by analyzing the associations between user data and resources and

filtering for similarity between users and resources. Even though this type of algorithm

is much better than the time series based algorithm, it still demands significant effort

on the part of an administrator because, initially, there is insufficient associated user

and resource data. In addition, there are a great many noisy and useless resources.

Reducing the amount of useless resources is difficult and achieving dynamic updates is

virtually impossible.

The third type is the collaborative filtering algorithm. This method takes good ad-vantage of similarities between users with similar preferences. It will provide new items

that have been previously viewed by other users who have the same preferences or

search demands as the active user. By using this method, an administrator only has

to match users with similar features. The advantages of this method are its high accu-

racy and ease of searching items that match user interests. These advantages make the

collaborative filtering algorithm a well-received and mainstream technique.

College of Engineering,Chengannur 1


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However, there are challenges associated with collaborative filtering algorithms:

Data sparsity. When a new user or item first enters the system, finding similar data

is difficult due to the lack of information, which gives rise to the cold start problem.

Extendibility. When there are billions of users and millions of items, the time com-

plexity will be very large. Many systems demand extremely rapid response to meet

performance agreement requirements without regard to the users purchasing record or

evaluation history. Consequently, a Collaborative Filtering (CF) recommendation sys-

tem must have high extendibility.

Similarity. Some items that are in fact very similar can have different names or

contexts, therefore, most recommendation systems would not be able to detect potentialassociations and would treat similar items as different.

SMART ARENA uses the hybrid collaborative algorithm and the suggestion algo-

rithm which we developed. Here, users can create their own profile. It is possible to

search and view movies and rate them. According to the rating given and by extracting

the user and item features, the recommendations are provided. The users can search

movies by different categories such as genre, director, year, IMDB rate, language, actor.



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2 LITERATURE SURVEY

2.1 Existing System

1 Netflix

Netflix[2] displays recommendations based on the movies ordered and watched by

users. The algorithm used in Netflix displays recommendations based on how users who

have watched the same film watch other films. The problems associated with Netflix

algorithms are:

1. Data sparsity. When a new user or item first enters the system, finding similar

data is difficult due to the lack of information, which gives rise to the cold start problem.

2. Extendibility. When there are billions of users and millions of items, the time

complexity will be very large. Many systems demand extremely rapid response to meet

performance agreement requirements without regard to the users purchasing record

or evaluation history. Consequently, a Collaborative Filtering (CF) recommendation

system must have high extendibility.

3. Similarity. Some items that are in fact very similar can have different names or con-

texts, therefore, most recommendation systems would not be able to detect potentialassociations and would treat similar items as different.

2 Jinni

Jinni is a recommendation engines for movies,TV shows and short films. Jinni search

includes semantic search, a meaning-based approach to interpreting queries by identi-

fying concepts within the content, rather than keywords.



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Figure 1: jinni.com

3 Movielens

Movielens is a recommender system and virtual community website that recommends

movies for its users to watch based on their film preferences using collaborative filtering.

Figure 2: movielens.org

4 Criticker

Criticker aims to match the user with the people who share his taste in film mostexactly. By using the Taste Compatibility Index(TCI), user can identify with whom



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he most agree, out of thousands. Its more than just movie recommendations: user is

paired with people whose tastes are most compatible with his own, and thus get the

most accurate advice possible.

Figure 3: criticker.com

5 IMDB

IMDB is an online database of information related to films, television programmes

and video games, including cast, production crew, fictional characters, biographies, plot

summaries and reviews.

Figure 4: imdb.com



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2.2 Proposed System

The implemented algorithm[1] improves the user-item similarity measurement by ex-

tracting item features and confirms item feature by calculating the weights of different

feature. User preference data is obtained by extracting item ratings. Recommendationsare made according to the accuWeigh algorithm, which is expected to improve recom-

mendation accuracy significantly. It can also handles the data sparsity problem and

can find potential semantic patterns in user ratings. Collectively, these features would

make predictions more understandable and more accurate.

The user preferences-item feature collaborative filtering algorithm implemented is

more accurate compared with previous traditional methods.

The main purpose of the newly proposed algorithm is to reveal the hidden rela-

tionship between the user model and the item model. It is evident that detailing of

information would greatly improve recommendation accuracy. Detailing of information

will be achieved by extracting the potential semantics of information. To some extent,

data sparsity can be solved by detailing the information, and detailing the information

has become a new research direction to address the challenging puzzles inherent in the

development recommendation systems.



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3 SOFTWARE REQUIREMENT SPECIFICATION

3.1 Introduction

3.1.1 Purpose of Project

The purpose of this document is to provide a detailed overview of our product and its

goals. This document describes the products features, its functions and user interface.

3.1.2 Scope of the Project

The proposed system gives the users the option to watch, rate and buy movies. The

system will give suggestions about movies to be watched on basis of their viewing history.

The recommendations are based on the recommendation algorithm which is a hybrid

collaborative filtering algorithm. The proposed system is better than the present system

as the algorithms presently used to provide recommendations have many disadvantages.

3.1.3 References

The following are the references:

1. Personalized Recommendation Algorithm Based on Preference Features Liang Hu,

Guohang Song, ZhenzhenXie, and Kuo Zhao June 2014

2. Web based Recommender Systems and Rating Prediction Tho Nguyen San Jose

State University January 2009

3. G. Linden, B. Smith, and J. York, Amazon.com recommendations: Item-to-item

collaborative filtering, IEEE Internet Computing, vol. 7, no. 1, pp. 76-80, 2003.

4. B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl, Recommender systems for

large-scale E-commerce: Scalable neighbourhood formation using clustering? in Pro-ceedings of the 5th International Conference on Computer and Information Technology

(ICCIT02), 2002

5. P. Resnick and H. R. Varian, Recommender systems, Communications of the ACM,

vol. 40, no. 3, pp. 56-58, 1997.

6. G. Adomavicius and A. Tuzhilin, Toward the next generation of recommender

systems: A survey of the stateof- theart and possible extensions, IEEE Transactions on

Knowledge and Data Engineering, vol. 40, no. 3, pp.169- 174, 1997.



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3.2 Overall Descriptions

3.2.1 Product Perspective

The proposed system, SMART ARENA is an online theatre where users will be able

to watch, buy and rate movies. The existing system make use of algorithms like time

series algorithm, user item based algorithm, collaborative filtering algorithm etc. The

major disadvantages of these algorithms are that they lack accuracy, do not meet users

demand, extendibility i.e. when huge data is there time complexity will me more.

The proposed system make use of personalized algorithm which is based on the user

preferences and item features. It gives highly accurate recommendations by improving

user-item similarity which is done by extracting item feature and applying various item

features weight to the item.

3.2.2 Product Functions

A website is designed for adding, rating, recommending and watching movies.

1. Login

A login page is designed wherein the user is requested to enter his username and

password.

2. Watch movie

The user could watch any movies of his interest.

3. Rating

Rating is done by the user on the basis of his preference, once he finished watching a

movie.

4. Adding Movies

Movies of different genres could be added by admin.

5. Search Movies

User can search movies by language, genre, actor, director, IMDB rating, year.



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6. Recommendation

Based on the preference of the user, the site recommends movies.

The recommendation is done by the following steps:

- The user preference for a particular genre is found out by using Term Frequency-

Inverse Document Frequency (TF-IDF). The following equation provides the definition

of user preference P(u,t) for on particular element t in movie items:

- The weight of element t to item I is determined.

- Similarity between users who have watched and rated the same movie is found outby using Pearsons correlation coefficient.

- Similarity between movies is found out by using Pearsons correlation coefficient.

- The two similarities are integrated together to find the prediction equation between

the user and the movie.

3.3 External Interface Requirements

3.3.1 User Interfaces

The login page where user enters the textual password consists of a login button. If

the login is correct then the person is directed to the users login page from where the

user can rate, add and watch movies. An error message is displayed if the login id is

entered incorrectly or if the password is wrong.

3.3.2 Software Interfaces

PHP

PHP (recursive acronym for PHP: Hypertext Preprocessor) is a widely-used open

source general-purpose scripting language that is especially suited for web development

and HTML can be embedded into it.



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MySQL

MySQL is the worlds most used relational database management system (RDBMS)

that runs as a server providing multi-user access to a number of databases.

Windows

Windows is an operating system produced by Microsoft for use on personal computers,

including home and business desktops, laptops, netbooks, tablet PCs, and media center

PCs.

3.4 Other Non-Functional Requirements

3.4.1 Security Requirements

There is a user authentication system for granting access to the user. It validates the

user and grants the user permission to access the users profile. Passwords are encrypted

and stored in the database which provides better security.



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4 SYSTEM DESIGN

System design is the process of planning a new system to complement or altogether

replace the old system. The purpose of the design phase is the first step in moving from

the problem domain to the solution domain. The design of the system is the criticalaspect that affects the quality of the software. System design is also called top-level

design. The design phase translates the logical aspects of the system into physical

aspects of the system.

4.1 Input Design

A major part in the design of the system is the preparation of the input. Input

is necessary for the successful development and implementation of the system. The

quality of the inputs determines the quality of the output. Input specifications describe

the manner in which data enter the system for processing. Inaccurate input is the

common cause of errors in data processing. The input design is the process of converting

user oriented inputs to a computer-based format. So the input interface design is the

important role in controlling errors.

Validations are made for each of every data enters to a new field so that he/she under-

standable what is to be entered whenever the user enters an error data. Error messages

are displayed and the user can move to next field only after entering the current data.

4.2 Output Design

Computer output is the most important and direct source of information to the user.

Without quality of output, the entire system may appear to be so unnecessary. It should

be developed while ensuring that each output element is designed so that people will

easy to use the system effectively. The term output applies to any information produced

by an information system, whether to be displayed or printed. The output form of the

system is either by screen or by hard copies.

Output design aims at communicating the results of the processing to the users. An

application is successful only it can produce efficient and effective outputs. The reports

are generated to suit the needs of the users. Output from the computer system is

required to communicate the result of processing to the user and to provide permanent

copy of these results for later consultation.



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4.3 Data Flow Diagram

Figure 5: DFD LEVEL 0




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4.4 Use-Case Diagram

Figure 8: Use Case Diagram for the System

A use case diagram at its simplest is a representation of a users interaction with the

system and depicting the specifications of a use case. A user can create a new account

by using sign up option or he can login with the existing account. Once the user logged

in to his account, he can view the recommended movies.Further,he has got the option

to search movies baased on genre,year,director,language,actor and imdb rating. He can

rate movies after viewing or searching.



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4.5 ER Diagram

Figure 9: ER DIAGRAM



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4.6 Database Tables

Table 1: Movie

Field Type Null Key DefaultMovid integer No Primary key Null

Name varchar(20) No Null

Year integer No Null

IMDB float No Null

Lang varchar(20) No Null

Sum varchar(20) No Null

Director varchar(20) No Null

POS float No Null

Link varchar(20) No Null

Genre varchar(20) No Null

Cast varchar(20) No Null

Table 2: Recom

Field Type Null Key Default

Username varchar(20) No Primary key Null

Genre varchar(20) No Primary key Null

Value Float No Null



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Table 3: Users


Username varchar(20) No Primary key Null

Password varchar(20) No NullContact integer No Null

Privilege varchar(20) No Null

Mail id varchar(20) No Null

Table 4: User feature

Field Type Null Key DefaultUser varchar(20) No Foreign key Null

Action float No Null

Adventure float No Null

Animation float No Null

Biographical float No Null

Comedy float No Null

Crime float No Null

Documentary float No Null

Drama float No Null

Fantasy float No Null

Historical float No Null

Horror float No Null

Musical float No Null

Mystery float No Null

Political float No Null

Romance float No Null

Science Fiction float No Null

Sports float No Null

Thriller float No NullWar float No Null

Western float No Null



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Table 5: Kart


User varchar(20) No Primary key Null

Mid

varchar(20) No Primary key NullGenre varchar(20) No Null

Rat float No Null

Table 6: Item feature


Movie integer No Foreign key NullAction float No Null

Adventure float No Null

Animation float No Null

Biographical float No Null

Comedy float No Null

Crime float No Null

Documentary float No Null

Drama float No Null

Fantasy float No Null

Historical float No Null

Horror float No Null

Musical float No Null

Mystery float No Null

Political float No Null

Romance float No Null

Science Fiction float No Null

Sports float No Null

Thriller float No Null

War float No NullWestern float No Null



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5 IMPLEMENTATION

Our project SMART ARENA is an implementation of the IEEE paper Personalized

Recommendation Algorithm Based on Preference Features[1]. It mainly focus on recom-

mending movies based on user preference, item preference, and the accuWeigh algorithmwhich we developed. User is provided with a rating option too.

Personalized service is an important trend in the development of information process-

ing technology. With the continuous rapid development and improvement of Internet

technology, there has been an explosive growth of information on the Internet.

Many recommendation systems suggest items to users by utilizing the techniques of

collaborative filtering (CF) based on historical records of items that the users haveviewed, purchased, or rated.

Recommendation systems try to recommend items (movies, music, webpages, prod-

ucts, etc) to interested customers, based on the information available. A successful

recommendation system can significantly improve the revenue of e-commerce compa-

nies or facilitate the interaction of users in online communities. Among recommendation

systems, content-based approaches analyze the content (e.g., texts, meta-data, features)

of the items to identify related items, while collaborative filtering uses the aggregated

behaviour/taste of a large number of users to suggest relevant items to specific users.

Collaborative filtering is popular and widely deployed in Internet companies like Ama-

zon, Netflix and others.

A thorough investigation of CF techniques preceeded the development of the algo-

rithm which we had implemented in our project. The implemented algorithm improves

the user item similarity measurement by extracting item features and confirms item fea-

ture by calculating the weights of different feature. User preference data is obtained by

extracting item ratings. Recommendations are made according to the item feature anduser preference, which is expected to improve recommendation accuracy significantly.

The implemented algorithm also handles the data sparsity problem. Collectively, these

features would make predictions more understandable and more accurate.



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5.1 Preference-Feature Model Development

User preference model development:In this section, we use Term Frequency-

Inverse Document Frequency (TF-IDF) to reference user us rating Rui for movie I as

the weight of the calculation of user preference for elements. The TF-IDF model iscommonly used for text character description. It is a statistical evaluation that pri-

marily shows the importance of some particular words to the whole text or a set in a

certain set. TF represents the occurrence frequency of a certain word in a given text,

and it shows the ability of this text to be distinguished from others. IDF represents

the appearance frequency of a particular word. It shows generality and decreases dis-

tinctiveness. Here we let TFut be the occurrence frequency of a particular element in

users ratings and IDF(t) be the occurrence frequency of one particular element in all

movies. The following equation provides the definition of user preference P(u,t) for one

particular element t in movie items:



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Item feature model development:The weight of an item element corresponds

to the user preference. Even though these two are similar, essentially user preference

represents the subjective attitude of users and the item element reflects the objective

truth of the user group, which is also an objective item property. In this section, we

will explain how to calculate the element features weight to the item using the user

preference matrix. TFit represents the rating weights of element t in all the rated

movies. IDF(t) represents the occurrence frequency of one particular element in all the

movies. Thus, P(u,t) can be defined as the user preference for some particular element

in movie items.We define Q(i,t) as the weight of element t to item i :



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5.2 Prediction of user rating

We assign different weights and 1 for the user preferences and item feature

similarity to calculate the integrated similarity. The first part represents the subjec-

tive feelings of the users and the second represents the objective elements of the items.

Through the weighted feature similarity calculation, we can derive the prediction equa-

tion for a user and an item:



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5.3 accuWeigh algorithm

The value of the prediction equation is compared with the rating values in the kart

database. The genres of the movies with the nearest rating value are filtered out.

The accuWeigh algorithm is applied on these genres individually and the correspondingvalues are stored in recom database. If two different values are obtained for a particular

user-genre combination the database is updated with the latest one.

accuWeigh algorithm equation:

Weight=(wckcount100)(tfratcf )

w=contribution of genre in all the films rated by that user

ck=No.of films in which genre is present

count=No.of films rated by the user

tfrat=Rating sum of all films rated by user

cf=total no.of genres in the films rated by the users

The genres with the highest values in the recom database is taken and all movies with

these genres are recommended to the user.



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6 SCREENSHOTS

Figure 10: Home page



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Figure 11: Login page



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Figure 12: Admin page



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Figure 13: Add Movie page



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Figure 14: Add Movie page



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Figure 15: Sign up page



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Figure 16: User profile and Recommendations



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Figure 17: Search Movie page



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Figure 18: Search results page



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Figure 19: Search by genre



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Figure 20: Search by language,director,year and IMDB rate



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Figure 21: Search by genre results



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Figure 22: Watch and Rate



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Figure 23: Watch Movie



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7 CONCLUSION and FUTURE SCOPE

The project SMART ARENA is used to provide accurate recommendations to the

user by using a hybrid collaborative filtering algorithm.In existing systems like Net-

flix,Jinni,Imdb and Movielens the recommendations are given only based on user ratingand similar users.It also require users to rate many films to give accurate recommenda-

tions.

In our project,we derived a system which allows the user to watch,rate and search

movie.It can give accurate recommendations based on users taste.Recommendations

are given based on user preference and item feature similarity.The accuWeigh algorithm

which we used in our project calculates the weight of genre of each user and recommen-

dations are provided based on accuWeigh result.Smart Arena is more accurate comparedwith previous traditional systems and it gets rid of the cold start problem.

Our system could be further expanded by adding few more features in to it.First,by

providing the function to recommend movies based on the genre of users.Second, by

providing recommendation based on the location of user.Third,by providing recommen-

dation based on the time of login.



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8 REFERENCES

[1] Personalized Recommendation Algorithm Based on Preference Features Liang Hu,

Guohang Song, ZhenzhenXie, and Kuo Zhao June 2014

[2] Web based Recommender Systems and Rating Prediction Tho Nguyen San Jose

State University January 2009

[3] G. Linden, B. Smith, and J. York, Amazon.com recommendations: Item-to-item

collaborative filtering, IEEE Internet Computing, vol. 7, no. 1, pp. 76-80, 2003.

[4] B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl, Recommender systems for

large-scale E-commerce: Scalable neighbourhood formation using clustering? in Pro-

ceedings of the 5th International Conference on Computer and Information Technology

(ICCIT02), 2002

[5] P. Resnick and H. R. Varian, Recommender systems, Communications of the ACM,

vol. 40, no. 3, pp. 56-58, 1997.

[6] G. Adomavicius and A. Tuzhilin, Toward the next generation of recommender

systems: A survey of the stateof- theart and possible extensions, IEEE Transactions on

Knowledge and Data Engineering, vol. 40, no. 3, pp.169- 174, 1997.

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SMART ARENA

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