Movie recommendation system using Collaborative filtering techniques in python

 

Movie recommendation system using Collaborative filtering techniques

 

 PROJECT ID: PYTHON17

 

PROJECT NAME: Movie recommendation system using Collaborative filtering techniques

 

PROJECT CATEGORY: MCA / BCA / BCCA / MCM / POLY / ENGINEERING

 

PROJECT ABSTRACT:

Recommender system uses the opinions of different age group users to help individuals more efficiently to find the item of their interest from a set of choices. There is a wide range of applications for recommendation system. They have become increasingly popular in last decades and are now used in every sector of online services that we approach to. These platforms are which contains movies ,music, books, social media etc. for example facebook ,it start recommending you pages similar to those you have liked recently or if u join a new organization it will start recommending you people in the same place.[1]

Two main approaches widely used for recommender systems. One is content based filtering where we try to recommend things according to his past history of purchase or like dislikes. Other one is collaborative based filtering where we try to group similar users and further use their information to make recommendations. ther types on the basis of approach are demographic filtering, hybrid  ecommender system , knowledge based recommender system. In demographic we don’t need information like reviews or rating it simply recommend by views of items. They are obtained by machine learning techniques. Hybrid systems are more efficient and overcome the issues faced in other techniques.[2] It’s a combination of other approaches. Collaborative and content -based method are mostly used in hybrid recommender system. Knowledge based systems work either on explicit recommendation rules or based on the common characteristics of user needs and products..

 

SOFTWARE REQUIREMENTS:

OS                                : Windows

Python IDE                  : Python 2.7.x and above

Language                             : Python Programming

Database                             : MYSQL

 

HARDWARE REQUIREMENTS:

 RAM                :  4GB and Higher

Processor          :  Intel i3 and above

Hard Disk         : 500GB Minimum

 

Dataset

We used the film –trust dataset for our experimentation. The data set consist user id ,movie id and ratings given to them. The dataset contains approx. 2100 user who has given around 35000 ratings to the movies they have seen. Dataset is in numeric form. The ratings range from 1-5 on the scale of 5 at the difference of .5. each user is represented by an id no other information is provided. No demographic knowledge or any other kind of data is shared. We split the dataset in ratio of 4:1 that is 80% of data is for training purpose and rest for experimenting. Larger datasets shows issues like scalability, time consumption

TABLE OF CONTENTS

·        Title Page      

·        Declaration

·        Certification Page

·        Dedication

·        Acknowledgements

·        Table of Contents

·        List of Tables

·        Abstract

 

CHAPTER SCHEME

CHAPTER ONE: INTRODUCTION

CHAPTER TWO: OBJECTIVES

CHAPTER THREE: PRELIMINARY SYSTEM ANALYSIS

·         Preliminary Investigation

·         Present System in Use

·         Flaws In Present System

·         Need Of New System

·         Feasibility Study

·         Project  Category

CHAPTER FOUR: SOFTWARE ENGINEERING AND PARADIGM APPLIED   

·         Modules

·         System / Module Chart

CHAPTER FIVE: SOFTWARE AND HARDWARE REQUIREMENT

CHAPTER SIX: DETAIL SYSTEM ANALYSIS

·         Data Flow Diagram

·         Number of modules and Process Logic

·         Data Structures  and Tables

·         Entity- Relationship Diagram

·         System Design

·         Form Design 

·         Source Code

·         Input Screen and Output Screen

CHAPTER SEVEN: TESTING AND VALIDATION CHECK

CHAPTER EIGHT: SYSTEM SECURITY MEASURES

CHAPTER NINE: IMPLEMENTATION, EVALUATION & MAINTENANCE

CHAPTER TEN: FUTURE SCOPE OF THE PROJECT

CHAPTER ELEVEN: SUGGESTION AND CONCLUSION

CHAPTER TWELE: BIBLIOGRAPHY& REFERENCES          

Other Information

 

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