movie recommender system using vectorization and SVD tech
- 1. DEPARTMENT OF COMPUTER SCIENCE, UNIVERSITY OF DELHI Movie Recommendation System Dr. Bharti Rana Submitted To: Uddeshy Kr. Bhagat(56) Pooja Sahu(33) MCA Semester 5 Submitted By:
- 2. About the Project • Introduction • Statement of the Problem • Objective • Related Work • Methodology • Summary and Conclusion
- 3. • A recommendation system is any system that automatically suggests content, products or serivces which should interest customers based on their preferences What is recommedation system? • Help to increase the site’s page views, dwell time, click-through rate, and retention • Help generate more advertising revenue • Increase upselling and crossselling Why recommendation system is useful for organization?
- 5. Approach 1 : Content Based
- 6. Approach 2 : Collaborative Filtering (CF) Based
- 7. Related Work Movie Recommendation Systems Using Actor-Based Matrix Computations in South Korea • The paper focuses on a content-based movie recommendation system in South Korea, utilizing data from the Korean Film Council, Naver movie site, and Korea Box Office Information System. • Data preprocessing steps include removing duplicates, excluding certain genres, and incorporating additional movie-related information, with a final dataset combining actor, director, genre, plot, and viewer-related details. • Correlation Analysis and User Evaluation: • The study involves computing rank correlation between movies and genres, as well as the correlation between actors and genres using Pearson's correlation coefficient. • Results reveal notable differences in correlation patterns between movies and genres compared to actors and movie genres. Additionally, a user survey is conducted to assess the recommendation system, with participants expressing preferences based on movie genres or actors. paper1
- 8. • The second paper discusses Collaborative Filtering (CF), specifically User- CF and Item-CF, as mainstream recommendation algorithms, focusing on personalized movie recommendations by identifying users with similar interests. • Singular Value Decomposition (SVD) is highlighted as a superior algorithm in the Movie Recommendation System based on the MovieLens dataset, outperforming Item-CF and User-CF, especially in scenarios with sparse data. Movie Recommendation System Based on SVD Collaborative Filtering Paper2
- 9. movies_metadata.csv • The main Movies Metadata file. Contains information on 45,000 movies featured in the Full MovieLens dataset. Features include posters, backdrops, budget, revenue, release dates, languages, production countries and companies. keywords.csv • Contains the movie plot keywords for our MovieLens movies. Available in the form of a stringified JSON Object. credits.csv • Consists of Cast and Crew Information for all our movies. Available in the form of a stringified JSON Object. links.csv • The file that contains the TMDB and IMDB IDs of all the movies featured. links_small.csv • Contains the TMDB and IMDB IDs of a small subset of 9,000 movies. ratings_small.csv • The subset of 100,000 ratings from 700 users on 9,000 movies. The Movies Dataset https://grouplens.org/datasets/movielens/latest/li
- 10. WorkFlow OF content based and improved Recommendation system
- 11. Stemming Stemming is a natural language processing technique that is used to reduce words to their base form, also known as the root form. The process of stemming is used to normalize text and make it easier to process.
- 12. For each document, a vector is created where each element represents the count of the corresponding term in the document. The result is a matrix where rows correspond to documents, and columns correspond to terms in the vocabulary. Here's a simple example: Suppose you have 4 documents: Document 1:"The greatest thing of life is love." Document 2: "Love is great, it's great to be loved." Document 3: "Is love the greatest thing?" Document 4:"I love lasagna for 1000 times." Count Vectorization
- 13. Cosine similarity is a metric used to measure how similar two vectors are, especially in the context of text data and document similarity. It calculates the cosine of the angle between two vectors, providing a measure of similarity irrespective of their magnitude Cosine similarity
- 14. Input: recommend(movie) takes a movie title (movie) as input for which the user wants recommendations. Output: The function prints the titles of 10 other movies that are most similar to the input movie. output Output of content based model
- 15. Improved Content Based It Uses Vote Count and vote average calculates weighted rating which can be calculated as: where, v is the number of votes for the movie m is the minimum votes required to be listed in the chart R is the average rating of the movie C is the mean vote across the whole report 𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑𝑅𝑎𝑡𝑖𝑛𝑔(𝑊𝑅) = (𝑣.R/(𝑣+𝑚))+(𝑚.C/(𝑣+𝑚))
- 16. Collaborative Filtering Based Our content based engine suffers from some severe limitations. • It is only capable of suggesting movies which are close to a certain movie. That is, it is not capable of capturing tastes and providing recommendations across genres. • Also, the engine that we built is not really personal in that it doesn't capture the personal tastes and biases of a user. Anyone querying our engine for recommendations based on a movie will receive the same recommendations for that movie, regardless of who (s)he is. • Therefore, in this section, we will use Collaborative Filtering to make recommendations to Movie Watchers. Collaborative Filtering is based on the idea that users similar to a me can be used to predict how much I will like a particular product or service those users have used/experienced but I have not
- 17. SVD (Singular Value Decomposition) is a popular matrix factorization technique commonly used in collaborative filtering-based movie recommendation systems. In a movie recommendation system, SVD is applied to decompose the user-item interaction matrix into three matrices: • User Matrix (U): Represents users and their latent factors. • Item Matrix (V): Represents items (movies) and their latent factors. • Singular Value Matrix (Σ): Represents the singular values. SVD Model The user-item interaction matrix M is decomposed as M is nearly equal to U.Σ.VT
- 18. Hybrid Movie Recommendation System Build a simple hybrid recommender that brings together techniques we have implemented in the content based and collaborative filter based engines.
- 19. Summary Content Based Recommender We built two content based engines; one that took movie overview and taglines as input and the other which took metadata such as cast, crew, genre and keywords to come up with predictions. We also devised a simple filter to give greater preference to movies with more votes and higher ratings. Improved Content Based Recommender This system used overall TMDB Vote Count and Vote Averages to build Top Movies Charts, in general and for a specific genre. The IMDB Weighted Rating System was used to calculate ratings on which the sorting was finally performed. In this Project, we have built 4 different recommendation engines based on different ideas and algorithms. They are as follows:
- 20. Summary Collaborative Filtering We used the powerful Surprise Library to build a collaborative filter based on single value decomposition. The RMSE obtained was less than 1 and the engine gave estimated ratings for a given user and movie. Hybrid Engine. We brought together ideas from content and collaborative filtering to build an engine that gave movie suggestions to a particular user based on the estimated ratings that it had internally calculated for that user.
- 21. Conclusion We see that for our hybrid recommender, we get different recommendations for different users although the movie is the same. Hence, our recommendations are more personalised and tailored towards particular users.
- 22. Thank You for listening!