Movie rating and recommendation system

Project Title

Movie rating and recommendation system

Project Area of Specialization Artificial IntelligenceProject Summary

Movie rating system where users are allowed to rate and comment on movies online. These ratings are provided as input to the website admin. The admin then checks reviews, critic’s ratings and displays an online rating for every movie. The purpose of this project is to develop an online system that automatically allows users to post reviews and store them.

The system will analyze this data (comments) to check for user sentiments associated with each comment and the system breaks user comments to check for sentiment keywords.

Once the keywords are found it associates the comment with a sentiment rank. After that the system gathers all comments for a particular movie and then calculates an average rating to score it. This score is generated for every movie in the system. This project will provide an automated movie rating system based on sentiment analysis.

Project Objectives

The Movie Recommendation System provides a mechanism to help users categorize users with similar interests. Basically the purpose of a recommendation system is to search for material that will be interesting to a person. Moreover, it involves a number of factors to create personalized lists of useful and interesting content specific to each user/individual. Recommendation systems are Artificial Intelligence based algorithms that skim through all possible options and create a customized list of items that are interesting and relevant to an individual. These results are based on their profile; search/browsing history, what other people with similar traits/demographics are watching, and how likely are you to watch those movies. This is achieved by applying item-based collaborative filtering.

Project Implementation Method

Our proposed system is to recommend appropriate movies to the users according to the ratings of the other users provided they are in same cluster. This needs consistent updates in the cluster and database. The target requires k-means clustering task.
K-means is one of the simplest unsupervised learning algorithms that solve the well-a set through a certain number of clusters. The goal of this algorithm is to find groups work of data, with the number of groups represented by the variable K. The algorithm works iteratively to assign each data point to one of groups based on the features that are provided.
Thus the goal of our system is to implement K-means clustering algorithm for sensible movie recommendations to newly and previously registered users, intake of the reviews (ratings and comments) from each user and updating the database or the cluster each time a review is taken.
Hence many recommendation systems have been developed over the past decade. These systems use different approaches like a collaborative approach, a content-based approach, a hybrid approach, etc.
There are a number of methodologies introduced to implement this recommendation system which includes various fields of Data Mining, Clustering and Bayesian Network methodology.

Benefits of the Project

• Work on several numbers of data:

The number of choices for anything on internet is very high and it’s tedious to refine most wanted data by self while searching. The scope of this proposal system includes working within numerous data, with ease.

• Saving of time:

Many people have problem selecting the alternative item of movie due to lack of time and due to search issues. Also movie recommendations from friends can be time consuming. The system helps in saving lots of time.

• Relief from processor problem:

Many mobile phone and limited processing power computers can’t handle recommender system due to its extremely large dataset. The solution opted for this can be use of web services. The proposed system uses web services, thus makes process simpler.

Technical Details of Final Deliverable

This recommendation system recommends different movies to users. Since this system is based on a collaborative approach, it will give progressively explicit outcomes contrasted with different systems that are based on the content-based approach. Content-based recommendation systems are constrained to people, these systems don't prescribe things out of the box. These systems work on individual users’ ratings, hence limiting your choice to explore more. While our system which is based on a collaborative approach computes the connection between different clients and relying upon their ratings, prescribes movies to others who have similar tastes, subsequently allowing users to explore more. It is a web application that allows users to rate movies as well as recommends them appropriate movies based on other's ratings.

Final Deliverable of the Project Software SystemCore Industry ITOther IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Industry, Innovation and InfrastructureRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	47000
Graphics processing unit	Equipment	1	20000	20000
Responsive Template Front end	Equipment	1	10000	10000
Responsive Template Back end	Equipment	1	10000	10000
Python Training / course	Miscellaneous	1	7000	7000