We built a recommender system that recommends movies to users based on historical ratings and tags data using information filtering techniques such as Collaborative Filtering, Content-Based Filtering and Singular Value Decomposition.
Overview of the Recommender system or recommendation system. RFM Concepts in brief. Collaborative Filtering in Item and User based. Content-based Recommendation also described.Product Association Recommender System. Stereotype Recommendation described with advantage and limitations.Customer Lifetime. Recommender System Analysis and Solving Cycle.
• Performed memory-based collaborative filtering techniques like Cosine similarities, Pearson’s r & model-based Matrix Factorization techniques like Alternating Least Squares (ALS) method
• Studied the scalability of these methods on local machines & on Hadoop clusters
Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
In this presentation we cover the following points:
→ introduction to recommender systems
→ working with explicit vs implicit feedback
→ content-based vs collaborative filtering approaches
→ user-based and item-item methods
→ machine learning and deep learning models
→ pros & cons of the methods: scalability, accuracy, explainability
Overview of the Recommender system or recommendation system. RFM Concepts in brief. Collaborative Filtering in Item and User based. Content-based Recommendation also described.Product Association Recommender System. Stereotype Recommendation described with advantage and limitations.Customer Lifetime. Recommender System Analysis and Solving Cycle.
• Performed memory-based collaborative filtering techniques like Cosine similarities, Pearson’s r & model-based Matrix Factorization techniques like Alternating Least Squares (ALS) method
• Studied the scalability of these methods on local machines & on Hadoop clusters
Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
In this presentation we cover the following points:
→ introduction to recommender systems
→ working with explicit vs implicit feedback
→ content-based vs collaborative filtering approaches
→ user-based and item-item methods
→ machine learning and deep learning models
→ pros & cons of the methods: scalability, accuracy, explainability
We have built an online Movie Recommender System which is based on the analysis of users' ratings history to several movies and their demographic information. We used data from Movielens website. Collaborative filtering and matrix factorization techniques have been used for the implementation. The end result is a web application where a user is recommended with top 20 movies.
Codebase: http://goo.gl/nM7RMy
Demo Video: http://goo.gl/VgZ2uI
In this lecture, I will first cover the recent advances in neural recommender systems such as autoencoder-based and MLP-based recommender systems. Then, I will introduce the recent achievement for automatic playlist continuation in music recommendation.
What really are recommendations engines nowadays?
This presentation introduces the foundations of recommendation algorithms, and covers common approaches as well as some of the most advanced techniques. Although more focused on efficiency than theoretical properties, basics of matrix algebra and optimization-based machine learning are used through the presentation.
Table of Contents:
1. Collaborative Filtering
1.1 User-User
1.2 Item-Item
1.3 User-Item
* Matrix Factorization
* Stochastic Gradient Descent (SGD)
* Truncated Singular Value Decomposition (SVD)
* Alternating Least Square (ALS)
* Deep Learning
2. Content Extraction
* Item-Item Similarities
* Deep Content Extraction: NLP, CNN, LSTM
3. Hybrid Models
4. In Production
4.1 Problematics
4.2 Solutions
4.3 Tools
Talk with Yves Raimond at the GPU Tech Conference on Marth 28, 2018 in San Jose, CA.
Abstract:
In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don't perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.
Past, present, and future of Recommender Systems: an industry perspectiveXavier Amatriain
Keynote for the ACM Intelligent User Interface conference in 2016 in Sonoma, CA. I start with the past by talking about the Recommender Problem, and the Netflix Prize. Then I go into the Present and the Future by talking about approaches that go beyond rating prediction and ranking and by finishing with some of the most important lessons learned over the years. Throughout my talk I put special emphasis on the relation between algorithms and the User Interface.
Shallow and Deep Latent Models for Recommender SystemAnoop Deoras
In this presentation, we survey latent models, starting with shallow and progressing towards deep, as applied to personalization and recommendations. After providing an overview of the Netflix recommender system, we discuss research at the intersection of deep learning, natural language processing and recommender systems and how they relate to traditional collaborative filtering techniques. We will present case studies in the space of deep latent variable models applied to recommender systems.
The Recommendation Engine is a tool which provides the various users a chance to buy different things and check what is in trend or what is liked by most of the people by going through the recommendations given to them on the basis of their past searches and other people’s buying history.
Recommender systems are software tools and techniques providing suggestions for items to be of interest to a user. Recommender systems have proved in recent years to be a valuable means of helping Web users by providing useful and effective recommendations or suggestions.
We have built an online Movie Recommender System which is based on the analysis of users' ratings history to several movies and their demographic information. We used data from Movielens website. Collaborative filtering and matrix factorization techniques have been used for the implementation. The end result is a web application where a user is recommended with top 20 movies.
Codebase: http://goo.gl/nM7RMy
Demo Video: http://goo.gl/VgZ2uI
In this lecture, I will first cover the recent advances in neural recommender systems such as autoencoder-based and MLP-based recommender systems. Then, I will introduce the recent achievement for automatic playlist continuation in music recommendation.
What really are recommendations engines nowadays?
This presentation introduces the foundations of recommendation algorithms, and covers common approaches as well as some of the most advanced techniques. Although more focused on efficiency than theoretical properties, basics of matrix algebra and optimization-based machine learning are used through the presentation.
Table of Contents:
1. Collaborative Filtering
1.1 User-User
1.2 Item-Item
1.3 User-Item
* Matrix Factorization
* Stochastic Gradient Descent (SGD)
* Truncated Singular Value Decomposition (SVD)
* Alternating Least Square (ALS)
* Deep Learning
2. Content Extraction
* Item-Item Similarities
* Deep Content Extraction: NLP, CNN, LSTM
3. Hybrid Models
4. In Production
4.1 Problematics
4.2 Solutions
4.3 Tools
Talk with Yves Raimond at the GPU Tech Conference on Marth 28, 2018 in San Jose, CA.
Abstract:
In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don't perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.
Past, present, and future of Recommender Systems: an industry perspectiveXavier Amatriain
Keynote for the ACM Intelligent User Interface conference in 2016 in Sonoma, CA. I start with the past by talking about the Recommender Problem, and the Netflix Prize. Then I go into the Present and the Future by talking about approaches that go beyond rating prediction and ranking and by finishing with some of the most important lessons learned over the years. Throughout my talk I put special emphasis on the relation between algorithms and the User Interface.
Shallow and Deep Latent Models for Recommender SystemAnoop Deoras
In this presentation, we survey latent models, starting with shallow and progressing towards deep, as applied to personalization and recommendations. After providing an overview of the Netflix recommender system, we discuss research at the intersection of deep learning, natural language processing and recommender systems and how they relate to traditional collaborative filtering techniques. We will present case studies in the space of deep latent variable models applied to recommender systems.
The Recommendation Engine is a tool which provides the various users a chance to buy different things and check what is in trend or what is liked by most of the people by going through the recommendations given to them on the basis of their past searches and other people’s buying history.
Recommender systems are software tools and techniques providing suggestions for items to be of interest to a user. Recommender systems have proved in recent years to be a valuable means of helping Web users by providing useful and effective recommendations or suggestions.
movie recommender system using vectorization and SVD techUddeshBhagat
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.
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.
talk at KTH 14 May 2014 about matrix factorization, different latent and neighborhood models, graphs and energy diffusion for recommender systems, as well as what makes good/bad recommendations.
Discover the Future of Entertainment: Dive into the world of movie recommendation systems in our engaging presentation. Join us as we explore the power of cutting-edge technology and data analytics to enhance user experiences in the entertainment industry. Our journey begins with data collection and cleaning, followed by a fascinating peek into the importance of movie recommendation systems.
Uncover the Problem: Have you ever felt overwhelmed by the sheer number of movie choices on streaming platforms like Netflix and Amazon Prime? Our project addresses this very challenge by simplifying your movie selection process.
A Glimpse into the Timeline: Journey with us through the phases of data collection, preprocessing, and basic exploratory data analysis. Witness the transformation of raw data into actionable insights.
Cosine Similarity Revealed: Delve into the heart of our recommendation system as we explain the concept of Cosine Similarity, the mathematical foundation behind our recommendations.
Pros and Cons Explored: Explore the pros and cons of movie recommendation systems, from personalized user experiences and increased engagement to challenges like the 'Cold Start Problem' and privacy concerns.
Siva Kumar Cheekula, Pavan Kapanipathi, Derek Doran, Amit P. Sheth. Entity Recommendations Using Hierarchical Knowledge Bases. In proceedings of 4th Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data at ESWC2015, Slovenia, May 31, 2015.
Paper at the 4th Know@LOD 2015.
Lessons learnt at building recommendation services at industry scaleDomonkos Tikk
Industry day keynote presentation held at ECIR 2016, Padova. The talk presents algorithmic, technical and business challenges Gravity R&D encountered from building a recommender system vendor company from being a top Netflix Prize contender.
Challenge@rule ml2015 rule based recommender systems for the Web of DataRuleML
Augmenting a feature set using mappings to the Web of data
is an up-and-coming way to enrich data in the original dataset. Those
enrichments are valuable especially for the recent preference learning
algorithms and recommender systems. In this paper, we describe the
process of mapping and augmenting the movie ratings dataset Movi-
eTweetings from the perspective of RecSysRules 2015 Challenge. The
ad-hoc queries to DBpedia are used as an underlying concept. To the
best of our knowledge, there is no existing mapping dataset of movies
for MovieTweetings.We also provide a brief discussion about the benets
of the augmented feature set for an elementary rule-based representation
of the user preferences.
These slides present Movie Recommender, a system which provides movie recommendations based on the information known about the users. These recommendations are done using the analysis of the users' psychological profile, their watching history and the movies scores from other websites. They are based on aggregate similarity calculation. The system uses both collaborative filtering and content filtering (using an approach based on different features of the movies from the database). Although there are similar applications available, they tend to ignore the data specific to the user, which in our opinion is essential for his/her behavior
Content Recommendation through Semantic Annotation of User Reviews and Linked...Iacopo Vagliano
The slides of my presentation at KCAP 2017
Nowadays, most recommender systems exploit user-provided ratings to infer their preferences. However, the growing popularity of social and e-commerce websites has encouraged users to also share comments and opinions through textual reviews. We introduce a new recommendation approach which exploits the semantic annotation of user reviews to extract useful and non-trivial information about the items to recommend. It also relies on the knowledge freely available in the Web of Data, notably in DBpedia and Wikidata, to discover other resources connected with the annotated entities. We evaluated our approach in three domains, using both DBpedia and Wikidata. The results showed that our solution provides a better ranking than another recommendation method based on theWeb of Data, while it improves in novelty with respect to traditional techniques based on ratings.
As the development of information technology and Internet,
people enter the era of information overload from that of
information deficiency gradually[17]. The report is the result of
the Master Thesis in Information and Communication
Technology School at Royal Institute of Technology in
Stockholm, Sweden. The project is provided in VionLabs AB
that is a media-tech company providing media content such as
movie information for customers. The recommender system we
implemented is for the movie website.
During the past several decades personnel function has been
transformed from a relatively obscure record keeping staff to central
and top-level management function. important resources. This
project intends to introduce more user friendliness in the various
activities such all the details required for the correct statement
calculation and generation. as record updation, maintenance, and
searching. The searching of record has been made quite simple as all
the details of the customer can be obtained by simply keying in the
identification or account number of that customer. Similarly, record
maintenance and updation can also be accomplished by using the
account number with all the details being automatically generated.
These details are also being promptly automatically updated in the
master file thus keeping the record absolutely up-to-date. The entire
information has maintained in the database or Files and whoever wants to retrieve can't retrieve, only authorization user can retrieve
the necessary information which can be easily be accessible from the
file.
A computer-based recommendation system is designed to handle
all the primary information required to calculate user's emotions.
Separate database is maintained to handle all the details required
for the correct statement calculation and generation.
This project intends to introduce more user friendliness in the
various activities such as record updation, maintenance, and
searching. The searching of record has been made quite simple
as all the details of the customer can be obtained by simply
keying in the identification or account number of that customer.
Similarly, record maintenance and updation can also be
accomplished by using the account number with all the details
being automatically generated. These details are also being
promptly automatically updated in the master file thus keeping
the record absolutely up-to-date.
The main objective of our project is providing the different typed
of customers facility, the main objective of this system is to find
out the actual customer service. Etc. It should fulfill almost all
the process requirements of any recommendation system.
User-based and Item-based collaborative-filtering algorithms are all neighborhood-
based algorithm, there are also a lot of other collaborative-filtering algorithms. Hoff-
man raised Latent Class Model in this paper[13], the model connects user and item
by latent class, which considers that a user will.
A recommender system or a recommendation system is a subclass of information filtering systems that seeks to predict the "rating" or "preference" a user would give to an item. (Wiki)
The goal of a Recommender System is to generate meaningful recommendations to a collection of users for items or products that might interest them. (Melville, Sindhwani)
Recommender systems reduce the information/choice overload by estimating the relevance
Similar to Movie Recommendation System - MovieLens Dataset (20)
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
3. Background
Top Streaming
Services
Need for a
recommendation system 13,000+ titles ~8 seconds
Netflix’s total content library Average human attention span
Data used in a
recommendation system
Impact of a
recommendation system
Watch
Data
Search
Data
Ratings
Data
Increased
Revenues
3
4. Data
• Source: https://grouplens.org/datasets/movielens/
• recommended for education and development
• Small: 100,000 ratings and 3,600 tag applications applied to 9,000 movies by
600 users. Last updated 9/2018.
•Movies
•movieId
•title
•genres
•Ratings
•userId
•movieId
•rating
•timestamp
•Tags
•userId
•movieId
•tag
•timestamp
•Links
•movieId
•imdbId
•tmdbId
9,742 100,836 1,587,923 9,742
4
5. Preliminary Analysis
Data
• Movies
Insights
• No of movies
released is
increasing every
year peaking at
733 in 2006.
• Sharp decline is
observed in
recent years
(could be due to
not including
recent releases in
the data)
5
6. Preliminary Analysis
Data
• Movies
Insights
• Drama, Comedy,
Thriller, Action
and Romance are
the top 5 genres
• The top 5 genres
together contain
~60% of the
movies
6
7. Preliminary Analysis
Data
• Movies
Insights
• Drama and
Comedy have
consistently
stayed the #1 and
#2 genres over
the years
• Similar
distribution of
genres over the
years
7
8. Preliminary Analysis
Data
• Movies
• Ratings
Insights
• Median > Mean
• Left skewed
distribution
• Do most users
rate most movies
on the higher end
of the 0-5 scale?
8
9. Preliminary Analysis
Data
• Movies
• Ratings
Insights
• Most users rate
less than 1000
movies
• Most users rate
movies on the
higher end of the
0-5 scale
• Most movies
receive less than
100 ratings
• Most movies are
rated on the
higher end of the
0-5 scale
9
10. Preliminary Analysis
Data
• Movies
• Ratings
Insights
• Average Ratings
for all genres is
between 3 and 4
• Among the top 5
genres, Drama
and Romance
movies have
higher ratings
compared to the
remaining three
#1 #2 #4#3#5
10
11. Preliminary Analysis
Data
• Movies
• Tags
Insights
• Tags can be useful
to create sub-
genres and drill
deeper into a
specific genre e.g.
Sci-Fi movies are
a huge sub-genre
within Action
movies
11
12. Part 2
• Content Based Filtering
• User Based Collaborative Filtering
• Item Based Collaborative Filtering
• Singular Value Decomposition
12
13. Model 1: Content Based Filtering (CBF) Approach
• Genre-based approach
• Without factoring release year, algorithm
recommends very old movies
• Term Frequency (TF) and Inverse Document
Frequency (IDF) used to determine the relative
importance of genres
• Vector Space Model
• Each movie represented by a vector of its
attributes
• For similar movies,
• Angle between their vectors is small
• Cosine of angle between their vectors is
large
13
1 movie
Movie Genre,
Release Year
CBF Algorithm
TF/IDF,
Cosine Similarity Score
n movies
Similar in Genres,
Closer in Release Years
Sort Results
Highest to Lowest
Cosine Similarity Scores
Filter Results
Select Top 20
Movies
Recommend Results
Display Movies in User’s
Watch Next List
14. Model 1: Content Based Filtering (CBF) Results
14
1. Rampage (2018)
2. Solo: A Star Wars Story (2018)
3. Ant-Man and the Wasp (2018)
4. Deadpool 2 (2018)
5. Sorry to Bother You (2018)
6. Pacific Rim: Uprising (2018)
7. A Wrinkle in Time (2018)
8. Jupiter Ascending (2015)
9. Avengers: Age of Ultron (2015)
10.Ant-Man (2015)
11.Power/Rangers (2015)
12.Turbo Kid (2015)
13.Hardcore Henry (2015)
14.Iron Man (2008)
15.Journey to the Center of the Earth (2008)
16.Mutant Chronicles (2008)
17.Outlander (2008)
18.Doctor Strange (2016)
19.Independence Day: Resurgence (2016)
20.Star Trek Beyond (2016)
Avengers: Infinity War - Part I (2018) Toy Story (1995) Insidious: Chapter 3 (2015)
1. Gordy (1995)
2. Reckless (1995)
3. Ninja Scroll (Jûbei ninpûchô) (1995)
4. Tale of Despereaux, The (2008)
5. Wild, The (2006)
6. Asterix and the Vikings (Astérix etlesVikings)
(2006)
7. Monsters, Inc. (2001)
8. The Good Dinosaur (2015)
9. Toy Story 2 (1999)
10.Shrek the Third (2007)
11.Moana (2016)
12.Adventures of Rocky and Bullwinkle,The-2000
13.Emperor's New Groove, The (2000)
14.Turbo (2013)
15.Antz (1998)
16.Jumanji (1995)
17.Indian in the Cupboard, The (1995)
18.Shrek (2001)
19.TMNT (Teenage Mutant Ninja Turtles)(2007)
20.Three Wishes (1995)
1. The Gallows (2015)
2. Frankenstein (2015)
3. Maggie (2015)
4. Body (2015)
5. Massu Engira Maasilamani (2015)
6. Into the Grizzly Maze (2015)
7. Return to Sender (2015)
8. Careful What You Wish For (2015)
9. Spotlight (2015)
10. Mojave (2015)
11. Knock Knock (2015)
12. Zipper (2015)
13. The Stanford Prison Experiment (2015)
14. Partisan (2015)
15. Bridge of Spies (2015)
16. The Perfect Guy (2015)
17. Silent Hill (2006)
18. Nightmare on Elm Street, A (2010)
19. Insidious (2010)
20. Paperhouse (1988)
15. Model 2: User-based Collaborative Filtering (UBCF)
Approach & Results
15
• Find look alike users based on similarity
• Recommend movies which user’s look-alike
has chosen in past.
• Very effective due to creation of user profiles
• Very time and resource consuming algorithm
as computations are made for every user pair.
Thus, we only take 20% of original data
• Results
User 1
• Avengers
• Age of Ultron
• Civil War
• Infinity War
• Iron Man
• Iron Man 2
• Iron Man 3
• Endgame
Not Watched
Watched
Watched
Recommend
Similar
Sample Data For Model 20% of Original Data
Model Train Data 80% of Sample Data
Model Test Data 20% of Sample Data
Root Mean Square Error 24167
16. Model 3: Item-based Collaborative Filtering (IBCF)
Approach & Results
16
• Like UBCF, but instead of finding user's look-
alike, we find a movie's look-alike.
• Recommend alike movies to user who has
rated this movie.
• Far less time and resource consuming than
UBCF but we’ve used the same 20% subset of
original data for model comparison
• Results
Watched
User 1 Avengers
• Age of Ultron
• Civil War
• Infinity War
• Endgame
Similar
Recommend
Sample Data For Model 20% of Original Data
Model Train Data 80% of Sample Data
Model Test Data 20% of Sample Data
Root Mean Square Error 29123
17. • Basic essence of SVD is to decomposes a
matrix of any shape into a product of 3
matrices with notable mathematical
properties: X = U S VT
• Decomposition of ratings matrix results in an
ordered matrix of a user feature matrix and
an item feature matrix which encapsulate the
variance associated with every direction of
the matrix
• Larger variances indicate less redundancy
and less correlation and hold features of data
• A representative subset of user rating
directions or principal components to
recommend movies is utilized
• Overall SVD aims to find the smallest
condensed subset of features by discarding
features imparting noise
17
Model 4: Singular Value Decomposition (SVD)
Approach
Movie
User
Sci-Fi
FemaleMale
Wonder
Woman
Captain
Marvel
Drama
Avengers
Endgame
Iron Man
Captain
America
Thelma &
Louise
Legally
Blonde
The
Shawshank
Redemption
Fight Club
18. Model 4: Singular Value Decomposition (SVD)
Results
Top rated movies by user ID 400
18
Recommended movies for user ID 400
19. Model Comparison & Recommendations
Model Proportion of Data RMSE
UBCF 20% 24167
IBCF 20% 29123
SVD 20% 0.91
19
• Movie recommendations are very subjective and vary from one user to another
• Each model has a different approach and its own set of pros and cons
• Weighing all the pros and cons, we would recommend SVD as it is a good mix of both collaborative filtering
methods
Background information of the project. The objective you want to achieve.
discussion of data source and nature of the variables involved in the analysis
GrouplLens - a research lab in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities specializing in recommender systems
MovieLens - a research site run by GroupLens Research, a unique research vehicle for dozens of undergraduates and graduate students researching various aspects of personalization and filtering technologies
Exploratory analysis of the data set, summary, plots, and maybe some kind of linear regression fit to check the feasibility of the problem as well as get a better idea of how this data looks.
Exploratory analysis of the data set, summary, plots, and maybe some kind of linear regression fit to check the feasibility of the problem as well as get a better idea of how this data looks.
Exploratory analysis of the data set, summary, plots, and maybe some kind of linear regression fit to check the feasibility of the problem as well as get a better idea of how this data looks.
Exploratory analysis of the data set, summary, plots, and maybe some kind of linear regression fit to check the feasibility of the problem as well as get a better idea of how this data looks.
Exploratory analysis of the data set, summary, plots, and maybe some kind of linear regression fit to check the feasibility of the problem as well as get a better idea of how this data looks.
Exploratory analysis of the data set, summary, plots, and maybe some kind of linear regression fit to check the feasibility of the problem as well as get a better idea of how this data looks.
Exploratory analysis of the data set, summary, plots, and maybe some kind of linear regression fit to check the feasibility of the problem as well as get a better idea of how this data looks.
Ratings are associated with both users and movies
While creating the best representation of our original features, we remove the unnecessary noise
By retaining features the features which have larger variances
Different genres in input -> Different genres in output as well
Holistic view