Recommendation systems provide users with information they may be interested in based on their preferences and interests. They help address the problem of information overload by retrieving desired information for the user based on their preferences or those of similar users. The two main types of recommendation systems are personalized and non-personalized systems. Common techniques used include collaborative filtering, which finds users with similar tastes, and content-based filtering, which recommends items similar to those a user has liked based on item attributes.
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.
Machine Learning based Hybrid Recommendation System
• Developed a Hybrid Movie Recommendation System using both Collaborative and Content-based methods
• Used linear regression framework for determining optimal feature weights from collaborative data
• Recommends movie with maximum similarity score of content-based data
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.
Machine Learning based Hybrid Recommendation System
• Developed a Hybrid Movie Recommendation System using both Collaborative and Content-based methods
• Used linear regression framework for determining optimal feature weights from collaborative data
• Recommends movie with maximum similarity score of content-based data
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
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.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
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.
It gives an overview of Sentiment Analysis, Natural Language Processing, Phases of Sentiment Analysis using NLP, brief idea of Machine Learning, Textblob API and related topics.
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
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.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
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.
It gives an overview of Sentiment Analysis, Natural Language Processing, Phases of Sentiment Analysis using NLP, brief idea of Machine Learning, Textblob API and related topics.
Recommender systems analyze patterns of user interest in
products to provide personalized recommendations. They seek to predict the rating or preference that user would
give to an item. Some of the most successful realizations of latent factor models are based on matrix factorization...
محاسبات عددی علم و هنر محاسبه است. محاسبات عددی (یا آنالیز عددی) به مطالعه ی روش ها و الگوریتم هایی گفته می شود که تقریب های عددی (در مقابل جواب های تحلیلی) را برای مسائل ریاضی بکار می برند. محاسبات عددی با اعمال شیوه های تقریبی محاسباتی به حل مسائلی از ریاضیات پیوسته می پردازد که به روش تحلیلی قابل حل نبوده و یا به سختی قابل حل تحلیلی هستند.
سرفصل هایی که در این آموزش به آن پرداخته شده است:
درس اول: خطاها و اشتباهات
درس دوم: حل دستگاه های معادلات خطی
درس سوم: درون یابی و برازش
درس چهارم: مشتق گیری و انتگرال گیری عددی
درس پنجم: حل عددی معادلات دیفرانسیل معمولی
...
برای توضیحات بیشتر و تهیه این آموزش لطفا به لینک زیر مراجعه بفرمائید:
http://faradars.org/courses/fvmth102
Neighbor methods vs matrix factorization - case studies of real-life recommen...Domonkos Tikk
This talk was given by István Pilászy, co-founder and head of core development at Gravity R&D, at LSRS workshop at Recsys 2015. Messages of the talk: (1) in industry item-2-item (i2i) recommendation is the dominant case, hardly researched by academia; (2) in industry you have typically implicit feedback data; (3) matrix factorization (MF) is good to optimize error metric, but less obvious for top-N and i2i recommendations. (4) item-kNN in most cases outperforms MF for i2i in terms of CTR; (5) Performance heavily depends on the domain and the recommendation scenario.
Spotify uses a range of Machine Learning models to power its music recommendation features including the Discover page and Radio. Due to the iterative nature of training these models they suffer from IO overhead of Hadoop and are a natural fit to the Spark programming paradigm. In this talk I will present both the right way as well as the wrong way to implement collaborative filtering models with Spark. Additionally, I will deep dive into how Matrix Factorization is implemented in the MLlib library.
Introduction to Recommendation Systems (Vietnam Web Submit)Trieu Nguyen
1) Why do we need recommendation systems ?
2) How can we think with recommendation systems ?
3) How can we implement a recommendation system with open source technologies ?
RFX framework https://github.com/rfxlab
Apache Kafka: https://kafka.apache.org
Apache Spark: https://spark.apache.org
Recommendations are everywhere : music, movies, books, social medias, e-commerce web sites… The Web is leaving the era of search and entering one of discovery. This quick introduction will help you to understand this vast topic and why you should use it.
Slides of my talk I gave at the big data conference: http://www.globalbigdataconference.com/santa-clara/5th-annual-global-big-data-conference/schedule-85.html
I have studies and analysed various recommender systems, and their pros and cons. Handy guide if you wish to have an introduction to recommender systems.
Social Media Mining - Chapter 9 (Recommendation in Social Media)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
I have studies and analysed various recommender systems, and their pros and cons. Handy deck if you wish to have an introduction to recommender systems.
Big data certification training mumbaiTejaspathiLV
ExcelR offers 160 hours classroom training on Business Analytics / Data Scientist / Data Analytics. We are considered as one of the best training institutes on Business Analytics in Mumbai. “Faculty and vast course agenda is our differentiator”. The training is conducted by alumni of premier institutions such as IIT & ISB who has extensive experience in the arena of analytics. They are considered to be one of the best trainers in the industry. The topics covered as part of this Data Scientist Certification program is on par with most of the Master of Science in Analytics (MS in Business Analytics / MS in Data Analytics) programs across the top-notch universities of the globe.
ExcelR is a proud partner of Universiti Malaysia Saravak (UNIMAS), Malaysia’s 1st public University and ranked 8th top university in Malaysia and ranked among top 200th in Asian University Rankings 2017 by QS World University Rankings. Participants will be awarded Data Science international certification from UNIMAS.
Variable , Array , Dictionary of swift -IOS Development - a hub for beginnerVikrant Arya
In this slide describe about some basic knowledge of swift programming.
I discussed about variables and array and dictionary.
If any problem come related to that feel free to mail me or comment.Will response fast ASAP.
You can also go on blog link
http://iosdevelopmenthub.blogspot.in/
This Presentation is written by Mr.Sudhir Agarwal.
He has great experience.ANd has lot of point that you have to consider when you start to write resume.
Read it in briefly and make your resume according to it.
That will be very helpful for you.
All the points cover in this Resume building presentation.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
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Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
2. What is a Recommmendation System?
Recommendation system is an information filtering technique, which
provides users with information, which he/she may be interested in.
Examples:
4. Why there is a need?
“Getting Information off the internet is like taking a drink from a fire
hydrant” - Mitchell Kapor
- Information Overload
- User Experience
- Revenues
Recommender systems help in addressing the information overload
problem by retrieving the information desired by the user based on his or
similar users' preferences and interests.
8. Techniques : Data Acquisition
1. Explicit Data
- Customer Ratings
- Feedback
- Demographics
- Physiographics
- Ephemeral Needs
2. Implicit Data
- Purchase History
- Click or Browse History
3. Product Information
- Product Taxonomy
- Product Attributes
- Product Descriptions
9. Techniques : Recommendation Generation
1. Collaborative Filtering method finds a subset of users who have
similar tastes and preferences to the target user and use this subset for
offering recommendations.
Basic Assumptions :
- Users with similar interests have common preferences.
- Sufficiently large number of user preferences are available.
Main Approaches :
- User Based
- Item Based
10. Techniques : Recommendation Generation
User-Based Collaborative Filtering
● Use user-item rating matrix
● Make user-to-user correlations
● Find highly correlated users
● Recommend items preferred by those users
Pearson Correlation :
Prediction Function :
11. Techniques : Recommendation Generation
User Based Collaborative Filtering
Item
User
I1 I2 I3 I4 I5
U1 5 8 7 8
U2 10 1
U3 2 2 10 9 9
U4 2 9 9 10
U5 1 5 1
User a 2 9 10
Recommend items preferred by highly correlated user U3 I5
12. Techniques : Recommendation Generation
User Based Collaborative Filtering
● Advantage :
- No knowledge about item features needed
● Problems :
- New user cold start problem
- New item cold start problem: items with few rating cannot easily be
recommended
- Sparsity problem: If there are many items to be recommended,
user/rating matrix is sparse and it hard to find the users who have rated
the same item.
- Popularity Bias: Tend to recommend only popular items.
e.g. RINGO, GroupLens
13. Techniques : Recommendation Generation
Item Based Collaborative Filtering
● Use user-item ratings matrix
● Make item-to-item correlations
● Find items that are highly correlated
● Recommend items with highest correlation
Similarity Metric :
Prediction Function :
14. Techniques : Recommendation Generation
Item Based Collaborative Filtering
Item
User
I1 I2 I3 Item I I5
U1 5 8 7 8
U2 10 1
U3 2 10 9 9
U4 2 9 9 10
U5 1 5 1
User a 2 9 10
Recommend items highly correlated to preferred items I5
15. Techniques : Recommendation Generation
Item Based Collaborative Filtering
● Advantages :
- No knowledge about item features needed
- Better scalability, because correlations between limited number of
items instead of very large number of users
- Reduced sparsity problem
● Problems :
- New user cold start problem
- New item cold start problem
e.g. Amazon, eBay
16. Techniques : Recommendation Generation
2. Content Based Systems recommend items similar to those a user has
liked (browsed/purchased) in the past.
OR
Recommendations are based on the content of items rather on other user's
opinion.
User Profiles: Create user profiles to describe the types of items that user
prefers.
e.g. User1 likes sci-fi, action and comedy.
Recommendation on the basis of keywords are also classified under content
based. e.g. Letizia
e.g. IMDB, Last.fm(scrobbler)
17. Techniques : Recommendation Generation
Content Based Systems Cont'd...
Advantages:
- No need for data on other users. No cold start and sparsity.
- Able to recommend users with unique taste.
- Able to recommend new and unpopular items.
- Can provide explanation for recommendation.
Limitations:
- Data should be in structured format.
- Unable to use quality judgements from other users.
19. Amazon's Demand and Solution
Demand :
● Amazon had more than 29 million customers and several million catalog
items.
● Amazon use recommendation algorithms to personalize the online store
for each customer in real time.
Solution :
● Existing algorithm were evaluated over small data sets.
● Reduce M by randomly sampling the customers or discarding
customers with few purchases. (M : the number of customers)
● Reduce N by discarding very popular or unpopular items. (N : the
number of items)
● Dimensionality reduction techniques such as clustering.
20. The Amazon Item-to-Item Collaborative Filtering Algorithm
Algorithm :
For each item in product catalog, I1
For each customer C who purchased I1
For each item I2 purchased by customer C
Record that a customer purchased I1 and I2
For each item I2
Compute the similarity between I1 and I2
Algorithm Complexity :
● Worst Case : O(N2M)
● In practice : O(NM), 'cause customers have fewer purchases.