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
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
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.
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
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
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.
An introduction to Recommendation engines
and how these systems work.
Both content based and collaborative filtering models are introduced.
Hotel recommendation system is explained as a case study.
It contains detail description about recommendation system which is a part of machine learning algorithms. It contains diagrams and examples for better understanding and description purpose with to the point detail matter.
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.
basic Function and Terminology of Recommendation Systems. Some Algorithmic Implementation with some sample Dataset for Understanding. It contains all the Layers of RS Framework well explained.
The goal of a recommender system is to predict the degree to which a user will like or dislike a set of items, such as movies or TV shows.
Most recommender systems use a combination of different approaches, but broadly speaking there are three different methods that can be used: Content analysis, Social recommendations and Collaborative filtering.
Recommendation systems, also known as recommendation engines, are a type of information system whose purpose is to suggest, or recommend items or actions to users.
The recommendations may consist of:
-> retail items (movies, books, etc.) or
-> actions, such as following other users in a social network.
It can be said that, Recommendation engines are nothing but an automated form of a “shop counter guy”. You ask him for a product. Not only he shows that product, but also the related ones which you could buy. They are well trained in cross selling and up selling. So, does our recommendation engines.
Movie Recommender System Using Artificial Intelligence Shrutika Oswal
In recent years, a huge amount of information is available on the internet and it is very difficult for the user to collect the relevant information. While purchasing any product also a lot of choices available and the user is confused about what to choose. This will be a time-consuming process as well. The search engine will solve this problem to some extent by but it will fail in giving a personalized recommendation. In this presentation, I will describe the different types and working of the recommender system how they gather the data, build recommender, generate recommendations from it, evaluate the performance and effectiveness of the recommender system. The further part of the presentation will describe how to build a movie recommender system using python.
This is a tutorial about recommender system for CS410 @ UIUC. It summarize some good research paper about how user profile and tags can improve recommender systems.
An introduction to Recommendation engines
and how these systems work.
Both content based and collaborative filtering models are introduced.
Hotel recommendation system is explained as a case study.
It contains detail description about recommendation system which is a part of machine learning algorithms. It contains diagrams and examples for better understanding and description purpose with to the point detail matter.
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.
basic Function and Terminology of Recommendation Systems. Some Algorithmic Implementation with some sample Dataset for Understanding. It contains all the Layers of RS Framework well explained.
The goal of a recommender system is to predict the degree to which a user will like or dislike a set of items, such as movies or TV shows.
Most recommender systems use a combination of different approaches, but broadly speaking there are three different methods that can be used: Content analysis, Social recommendations and Collaborative filtering.
Recommendation systems, also known as recommendation engines, are a type of information system whose purpose is to suggest, or recommend items or actions to users.
The recommendations may consist of:
-> retail items (movies, books, etc.) or
-> actions, such as following other users in a social network.
It can be said that, Recommendation engines are nothing but an automated form of a “shop counter guy”. You ask him for a product. Not only he shows that product, but also the related ones which you could buy. They are well trained in cross selling and up selling. So, does our recommendation engines.
Movie Recommender System Using Artificial Intelligence Shrutika Oswal
In recent years, a huge amount of information is available on the internet and it is very difficult for the user to collect the relevant information. While purchasing any product also a lot of choices available and the user is confused about what to choose. This will be a time-consuming process as well. The search engine will solve this problem to some extent by but it will fail in giving a personalized recommendation. In this presentation, I will describe the different types and working of the recommender system how they gather the data, build recommender, generate recommendations from it, evaluate the performance and effectiveness of the recommender system. The further part of the presentation will describe how to build a movie recommender system using python.
This is a tutorial about recommender system for CS410 @ UIUC. It summarize some good research paper about how user profile and tags can improve recommender systems.
FIND MY VENUE: Content & Review Based Location Recommendation SystemIJTET Journal
Abstract—Recommender system is a software application agent that presents the culls, interest and predilections of individual persons/ users and makes recommendation accordingly. During the online search they provide more facile method for users to make decisions predicated on their recommendations. Collaborative filtering (CF) technique is utilized, which is predicated on past group community opinions for utilizer and item and correlates them to provide results to the utilizer queries. Here the LARS is a location cognizant recommender system to engender location recommendation by utilizing location predicated ratings within a single framework. The system suggests k items personalized for a querying utilizer u. For traditional system which could not fortify spatial properties of users, community opinion can be expressed through triple explicit ratings that are (utilizer, rating, item) which represents a utilizer providing numeric ratings for an item. LARS engenders recommendation through taxonomy of three types of location predicated ratings. Namely spatial ratings for non-spatial items, non-spatial ratings for spatial items, spatial ratings for spatial items. Through this LARS can apply with the Content & Review Predicated Location Recommendation System. Which gives a culled utilizer a group of venues or ads by giving thought to each personal interest and native predilection. This system deals with offline modeling and on-line recommendation. To get the instant results, a ascendable question process technique is developed by elongating each the edge rule with Threshold Algorithm.
Architecting AI Solutions in Azure for BusinessIvo Andreev
The topic is about Azure solution architectures that involve IoT and AI to solve common business domain problems. With near real time recommender system and an object detection with image recognition we review the architecture, build from the ground-up and illustrate how the typical realistic challenges could be addressed.
Movie Recommendation System Using Hybrid Approch.pptxChanduChandran6
In this busy world, entertainment is essential for each of us to refresh our mood and energy. Having fun can restore confidence at work and work with more enthusiasm. To rejuvenate ourselves, we watch our favorite movies. To watch favorable movies online, we can take advantage of more reliable movie recommendation systems, because searching for preferred movies takes more time and cannot be wasted. In this project, a hybrid approach combining content-based filtering and collaborative filtering is presented to improve the quality of a movie recommendation system. Along with that sentiment analysis is done using Bayes Algorithm and Cosine Similarity. The proposed approach shows improvement over pure approaches in the accuracy, quality, and scalability of the movie recommendation system. Three different ones datasets are using in this system. Hybrid approach helps to gain advantages from both approaches and eliminate the disadvantages of both methods.
Modern Perspectives on Recommender Systems and their Applications in MendeleyKris Jack
Presentation given for one of Pearson's Data Research teams. It motivates the use of recommender systems, describes common approaches to building and evaluating them and gives examples of how they are used in Mendeley. Thanks to Maya Hristakeva for creating some of the slides.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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
3. • Sentiment Analysis
Clustering
• Collaborative-based filtering
• Item based
• User Based
Recommendation
Similarity
Measurement–Pearson, Tanimoto
Algorithm - K-means
Similarity Measurement - Euclidean
Classification NLP
• Content-based filtering
• Regression
• Decision Tree
• SVM
• NN
• Voice Recognition
• Video Analytics
4. Content Based, Collaborative
Filtering[CF] and Hybrid
Recommendation System
• Content Based systems focus on properties of items.
Similarity of items is determined by measuring the
similarity in their properties.
Needs History Data.
• Collaborative-Filtering systems focus on the
relationship between users and items. Similarity of
items is determined by the similarity of the ratings of
those items by the users who have rated both items.
Source-http://infolab.stanford.edu/~ullman/mmds/ch9.pdf
5. How users are similar?
CF - User Similarity
Similarity Notion
User
Neighborhood
User Based
Recommender
#1 #2 #3
User Id Item Id Rating
Data Model
6. CF - Item Similarity
How items are similar?
Similarity Notion
Item Based
Recommender#1 #2 #3
User Id Item Id Rating
Data Model
Item-neighborhood
Source-http://www.theregister.co.uk/2006/08/15/beer_diapers/
7. Similarity Notion
• Pearson Correlation - measures the tendency of the numbers[User
Preferences] to move together proportionally. When this tendency is high, the correlation is
close to 1
• Spearman Correlation – Rank based on user preference
• Euclidean Distance - based on the distance between users. Smaller the
distance, more similarity in users.
• Tanimoto Coefficient – based on number of items in common
• LogLikelihood Similarity
How to code?
8. How Similarity Definition affects
Neighborhood formation?
Source: http://www.slideshare.net/Cataldo/apache-mahout-tutorial-recommendation-20132014
Mahout In Action
Threshold based
neighborhood
9. Evaluation
• Evaluate Top n Recommendations
• Precision and Recall
Relevant Non Relevant
Search Result Shown
True Positive False Positive
Search result Not Shown
False Negative True Negative
Source-https://en.wikipedia.org/wiki/Precision_and_recall
10. System Solutioning - More than
Algorithm Accuracy
• Business Goal Injection
• Novelty – avoiding repeated recommendations
• Diversity – How diverse are recommended items?
Does it include all sub topics?
• Positive Feedback
• Negative Feedback
source: http://www.slideshare.net/Zhenv5/diversity-and-novelty-for-recommendation-system
11. Technology
• Mahout –
Hadoop(optional), Java.
Lot of stable algorithms.
• R
Rhadoop
Lot of Statistics packages.
• Spark
Emerging Technology
Algorithms are getting added
Editor's Notes
Recommendation borrows lot of statistical methods and ML techniques. So same algo can occur in clustering or new one. Algo we disccuss today and frequqnetly used one.
How many of you like gmail, android and google search. Tech Talk, Technical Blogs, Books. I do not know buying behavior, but social behavior.
Facebook news. Tech talkl, blogs, reading books. Next certification cource.
Baby items. Beer and diaper. Evaluate both User Similarity vs Item Similarity. How to keep items in same isle.
School of thought. Preference values. Rank.
Baby items for old age people, or gap between two kids, two laptop purshases. Google searches.