The document discusses group recommender systems. It begins with an overview of recommender systems principles and introduces the concept of group recommendation. It then outlines several key tasks in group recommendation systems, including defining different types of groups, acquiring preferences, modeling groups, predicting ratings, helping groups reach consensus, and explaining recommendations to groups. The document provides examples of approaches used in existing systems for each of these tasks. It also surveys common techniques for modeling groups, such as additive utilitarian, multiplicative utilitarian, Borda count, and Copeland rule strategies.
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
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
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
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.
Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Se...Sudeep Das, Ph.D.
In this talk, we will provide an overview of Deep Learning methods applied to personalization and search at Netflix. We will set the stage by describing the unique challenges faced at Netflix in the areas of recommendations and information retrieval. Then we will delve into how we leverage a blend of traditional algorithms and emergent deep learning methods and new types of embeddings, especially hyperbolic space embeddings, to address these challenges.
Recommendation Systems - Why How and Real Life ApplicationsLiron Zighelnic
These slides were created for a presentation at MIT - Massachusetts Institution of Technology
- Data Analytics Club
Recommendations become very popular in almost every field of our lives, from movies, to news to dating. Many systems try to give us personal recommendations.
In this presentation we will examine:
- Why recommendations are important?
- What are the main methods and algorithms being used?
- Real life applications & who use it? (the question should be: who doesn’t?)
About CurtainApp:
CurtainApp is an intelligent mobile app that learns your taste and gives you personal fashion recommendations, making shopping fun and efficient
Visit: www.curtainapp.com
Join us on Facebook: facebook.com/CurtainApp
Follow us on Twitter: twitter.com/thecurtainapp
#MIT #mobileapp #recommendation #fashion #recommendersystems #paradoxofchoice #Google #Netflix #OkCupid #Pandora #Curtain
[Phd Thesis Defense] CHAMELEON: A Deep Learning Meta-Architecture for News Re...Gabriel Moreira
Presentation of the Phd. thesis defense of Gabriel de Souza Pereira Moreira at Instituto Tecnológico de Aeronáutica (ITA), on Dec. 09, 2019, in São José dos Campos, Brazil.
Abstract:
Recommender systems have been increasingly popular in assisting users with their choices, thus enhancing their engagement and overall satisfaction with online services. Since the last decade, recommender systems became a topic of increasing interest among machine learning, human-computer interaction, and information retrieval researchers.
News recommender systems are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, it is a challenging scenario for recommendations. Large publishers release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated and irrelevant to most readers. News readers exhibit more unstable consumption behavior than users in other domains such as entertainment. External events, like breaking news, affect readers interests. In addition, the news domain experiences extreme levels of sparsity, as most users are anonymous, with no past behavior tracked.
Since 2016, Deep Learning methods and techniques have been explored in Recommender Systems research. In general, they can be divided into methods for: Deep Collaborative Filtering, Learning Item Embeddings, Session-based Recommendations using Recurrent Neural Networks (RNN), and Feature Extraction from Items' Unstructured Data such as text, images, audio, and video.
The main contribution of this research was named CHAMELEON a meta-architecture designed to tackle the specific challenges of news recommendation. It consists of a modular reference architecture which can be instantiated using different neural building blocks.
As information about users' past interactions is scarce in the news domain, information such as the user context (e.g., time, location, device, the sequence of clicks within the session), static and dynamic article features like the article textual content and its popularity and recency, are explicitly modeled in a hybrid session-based recommendation approach using RNNs.
The recommendation task addressed in this work is the next-item prediction for user sessions, i.e., "what is the next most likely article a user might read in a session?". A temporal offline evaluation is used for a realistic offline evaluation of such task, considering factors that affect global readership interests like popularity, recency, and seasonality.
Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based algorithms.
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.
Collaborative Recommender System for Music using Pytorch. Combine Matrix Factorization and Neural Networks for improved performance. Python sample code included.
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.
Deep learning: the future of recommendationsBalázs Hidasi
An informative talk about deep learning and its potential uses in recommender systems. Presented at the Budapest Startup Safary, 21 April, 2016.
The breakthroughs of the last decade in neural network research and the quick increasing of computational power resulted in the revival of deep neural networks and the field focusing on their training: deep learning. Deep learning methods have succeeded in complex tasks where other machine learning methods have failed, such as computer vision and natural language processing. Recently deep learning has began to gain ground in recommender systems as well. This talk introduces deep learning and its applications, with emphasis on how deep learning methods can solve long standing recommendation problems.
KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Begin...Simplilearn
This K-Nearest Neighbor Classification Algorithm presentation (KNN Algorithm) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into these slides to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial:
1. Why do we need KNN?
2. What is KNN?
3. How do we choose the factor 'K'?
4. When do we use KNN?
5. How does KNN algorithm work?
6. Use case - Predict whether a person will have diabetes or not
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Learn more at: https://www.simplilearn.com
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Se...Sudeep Das, Ph.D.
In this talk, we will provide an overview of Deep Learning methods applied to personalization and search at Netflix. We will set the stage by describing the unique challenges faced at Netflix in the areas of recommendations and information retrieval. Then we will delve into how we leverage a blend of traditional algorithms and emergent deep learning methods and new types of embeddings, especially hyperbolic space embeddings, to address these challenges.
Recommendation Systems - Why How and Real Life ApplicationsLiron Zighelnic
These slides were created for a presentation at MIT - Massachusetts Institution of Technology
- Data Analytics Club
Recommendations become very popular in almost every field of our lives, from movies, to news to dating. Many systems try to give us personal recommendations.
In this presentation we will examine:
- Why recommendations are important?
- What are the main methods and algorithms being used?
- Real life applications & who use it? (the question should be: who doesn’t?)
About CurtainApp:
CurtainApp is an intelligent mobile app that learns your taste and gives you personal fashion recommendations, making shopping fun and efficient
Visit: www.curtainapp.com
Join us on Facebook: facebook.com/CurtainApp
Follow us on Twitter: twitter.com/thecurtainapp
#MIT #mobileapp #recommendation #fashion #recommendersystems #paradoxofchoice #Google #Netflix #OkCupid #Pandora #Curtain
[Phd Thesis Defense] CHAMELEON: A Deep Learning Meta-Architecture for News Re...Gabriel Moreira
Presentation of the Phd. thesis defense of Gabriel de Souza Pereira Moreira at Instituto Tecnológico de Aeronáutica (ITA), on Dec. 09, 2019, in São José dos Campos, Brazil.
Abstract:
Recommender systems have been increasingly popular in assisting users with their choices, thus enhancing their engagement and overall satisfaction with online services. Since the last decade, recommender systems became a topic of increasing interest among machine learning, human-computer interaction, and information retrieval researchers.
News recommender systems are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, it is a challenging scenario for recommendations. Large publishers release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated and irrelevant to most readers. News readers exhibit more unstable consumption behavior than users in other domains such as entertainment. External events, like breaking news, affect readers interests. In addition, the news domain experiences extreme levels of sparsity, as most users are anonymous, with no past behavior tracked.
Since 2016, Deep Learning methods and techniques have been explored in Recommender Systems research. In general, they can be divided into methods for: Deep Collaborative Filtering, Learning Item Embeddings, Session-based Recommendations using Recurrent Neural Networks (RNN), and Feature Extraction from Items' Unstructured Data such as text, images, audio, and video.
The main contribution of this research was named CHAMELEON a meta-architecture designed to tackle the specific challenges of news recommendation. It consists of a modular reference architecture which can be instantiated using different neural building blocks.
As information about users' past interactions is scarce in the news domain, information such as the user context (e.g., time, location, device, the sequence of clicks within the session), static and dynamic article features like the article textual content and its popularity and recency, are explicitly modeled in a hybrid session-based recommendation approach using RNNs.
The recommendation task addressed in this work is the next-item prediction for user sessions, i.e., "what is the next most likely article a user might read in a session?". A temporal offline evaluation is used for a realistic offline evaluation of such task, considering factors that affect global readership interests like popularity, recency, and seasonality.
Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based algorithms.
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.
Collaborative Recommender System for Music using Pytorch. Combine Matrix Factorization and Neural Networks for improved performance. Python sample code included.
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.
Deep learning: the future of recommendationsBalázs Hidasi
An informative talk about deep learning and its potential uses in recommender systems. Presented at the Budapest Startup Safary, 21 April, 2016.
The breakthroughs of the last decade in neural network research and the quick increasing of computational power resulted in the revival of deep neural networks and the field focusing on their training: deep learning. Deep learning methods have succeeded in complex tasks where other machine learning methods have failed, such as computer vision and natural language processing. Recently deep learning has began to gain ground in recommender systems as well. This talk introduces deep learning and its applications, with emphasis on how deep learning methods can solve long standing recommendation problems.
KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Begin...Simplilearn
This K-Nearest Neighbor Classification Algorithm presentation (KNN Algorithm) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into these slides to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial:
1. Why do we need KNN?
2. What is KNN?
3. How do we choose the factor 'K'?
4. When do we use KNN?
5. How does KNN algorithm work?
6. Use case - Predict whether a person will have diabetes or not
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Learn more at: https://www.simplilearn.com
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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
Design of Automated Sentiment or Opinion Discovery System to Enhance Its Perf...idescitation
In today’s social networking era, if one has to make
decision about any product, service or individual performance,
the availability of various comments, suggestions, ratings,
and feedbacks are abundant. The required decision support
data can be collected through different sources of Medias like
newspapers, blogs, and discussion forums and from internet
too. So surely, it leads to the selection of best product, service
or individual if it is analyzed efficiently. In leading and
competitive world, this is huge and practical need of industries,
organizations to empower their qualities. In the recent years,
the significant study is done in the field of sentiment analysis.
However, the earlier work focused the implementation and
evaluation of individual sub technique of sentiment analysis.
Though these implementations produces significant results
of sentiment or opinion analysis, the trust of decision makers
is still in dangling to accept the results of such analysis. In
this paper, initially, we have been described the brief review
about the sentiment or opinion analysis system. Then the
details are provided about the design and about how to build
an automated opinion discovery system to enhance
performance of sentiment or opinion analysis based on feature
extraction sentiment analysis sub technique, natural language
processing and data mining techniques in an integrated way
Music Recommendation System with User-based and Item-based Collaborative Filt...ijeei-iaes
Internet and E-commerce are the generators of abundant of data, causing information Overloading. The problem of information overloading is addressed by Recommendation Systems (RS). RS can provide suggestions about a new product, movie or music etc. This paper is about Music Recommendation System, which will recommend songs to users based on their past history i.e. taste. In this paper we proposed a collaborative filtering technique based on users and items. First user-item rating matrix is used to form user clusters and item clusters. Next these clusters are used to find the most similar user cluster or most similar item cluster to a target user. Finally songs are recommended from the most similar user and item clusters. The proposed algorithm is implemented on the benchmark dataset Last.fm. Results show that the performance of proposed method is better than the most popular baseline method.
"If I like BLANK, what else will I like?": Analyzing a Human Recommendation C...Toine Bogers
While there have been several studies on how users experience algorithmic recommendations and their explanations, we know relatively little about human recommendations and which item aspects humans highlight when describing their own recommendation needs. A better understanding of human recommendation behavior could help us design better recommender systems that are more attuned to their users. In this paper, we take a step towards such understanding by analyzing a Reddit community dedicated to requesting and providing for recommendations: /r/ifyoulikeblank. After a general analysis of the community, we provide a more detailed analysis of the prevalent music requests and the example items used to ask for these recommendations. Finally, we compare these human recommendations to algorithmic recommendations to better char- acterize their differences. We conclude by discussing the implications of our work for recommender systems design.
A SURVEY OF MACHINE LEARNING TECHNIQUES FOR SENTIMENT CLASSIFICATIONijcsa
Opinion Mining also called as Sentiment Analysis is a process that provides with the subjective informationfor the text provided. In other words we can say that it analyzes person’s opinion, evaluations, emotions,appraisals, etc. towards a particular product, event, issue, service, topic, etc. This paper focuses on the machine learning techniques used for sentiment analysis and opinion mining. These methods are furthercompared on the basis of their accuracy, advantages and limitations.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
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Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Challenges and Solutions in Group Recommender Systems
1. CHALLENGES AND SOLUTIONS IN
GROUP RECOMMENDER SYSTEMS
Ludovico Boratto (ludovicoboratto.com – ludovico.boratto@acm.org)
Eurecat (Spain)
ICDM 2017 – 17th IEEE International Conference on Data Mining
2. Plan of the talk
1. Recommender systems principles
2. Group recommendation introduction
3. Tasks and state of the art survey
4. Evaluation methods
5. Emerging aspects and techniques
6. Case study
7. Summary
10. Recommender Systems
¨ In everyday life we rely on recommendations
from other people either by word of mouth,
recommendation letters, movie and book reviews
printed in newspapers, ...
¨ In a typical recommender system people provide
recommendations as inputs, which the system
then aggregates and directs to appropriate
recipients
11. Recommender Systems
¨ A recommender system helps to make choices
without sufficient personal experience of the
alternatives
¤ To suggest products to their customers
¤ To provide consumers with information to help them
decide which products to purchase
¨ They are based on a number of technologies:
information filtering, machine learning, adaptive
and personalized system, user modeling, …
12. The recommendation problem
¨ We are given:
¤ a set of users
¤ a set of items
¤ a set of values (e.g., V=[1,5] or V={like,dislike})
¨ Let be a ternary relation that contains the
preferences given by the users
¨ We denote as the subset of items evaluated by a
user u
¨ The objective is to define a function
(prediction of the unknown ratings) and to identify an
item i* with the highest predicted rating:
U = {u1,u2,...,un}
I = {i1,i2,...,im}
V
R ⊆U × I ×V
Iu
f :U × I →V
i* = argmax
j∈I Iu
f (u, j)
13. Core Recommendation Techniques
¨ U is a set of users
¨ I is a set of items/products
Technique Background Input Process
Collaborative Ratings from U of items in I Ratings from u of items in I Identify users in U similar to u,
and extrapolate from their
ratings of i
Content-based Features of items in I u’s ratings of items in I Generate a classifier that fits
u’s rating behavior and use it
on i
Demographic Demographic information
about U and their ratings of
items in I
Demographic information
about u
Identify users that are
demographically similar to u,
and extrapolate from their
ratings of i
Utility-based Features of items in I A utility function over items
in I that describes u’s
preferences
Apply the function to the items
and determine i’s rank
Knowledge-
based
Features of items in I.
Knowledge of how these items
meet a user’s needs
A description of u’s needs
or interests
Infer a match between i and
u’s need
15. Group Recommendation
¨ Designed for contexts in which more than one
person is involved in the recommendation process
I’m a
vegetarian!
I’m on a
diet
I love Asian
food
Where shall we dine?
16. Group Recommendation
Application scenarios
¨ Any scenario that involves a decision making process
and a group of users
¤ People dining together (“Where shall we dine?”)
¤ Friends going to the cinema (“Which movie shall we
watch?”)
¤ Groups planning a trip (“Where shall we go?”)
¤ …
17. Group Recommendation
Problem statement
¨ We are given:
¤ a set of users
¤ a set of items
¤ a set of values (e.g., V=[1,5] or V={like,dislike})
¨ Let be a ternary relation that contains
the preferences given by the users
U = {u1,u2,...,un}
I = {i1,i2,...,im}
V
R ⊆U × I ×V
18. Group Recommendation
Problem statement
¨ Let the set of users U be split into K groups, where
each group respects the following properties:
¤ all the users in gk receive the same recommendations
¤ each user in U has to belong to a group in order to
receive the recommendations:
¤ groups are formed by sets of users who don’t intersect
(each user receives just one set of recommendations):
gk ⊆ U
∀u ∈U ∃ k ∈ {1,...,K} s.t. u ∈ gk
∀k,q ∈ {1,...,K} k ≠ q ⇒ gk ∩gq = ∅
19. Group Recommendation
Problem statement
¨ Given a group the objective is to define a
function and to identify an item i* with
the highest predicted rating:
gk ⊆ U
f :gk × I →V
i* = argmax
j∈I
f (gk, j)
20. Group Recommendation
Challenges
1. How should the different types of group be handled
in the recommendation process?
2. Should the preferences be collected for each user or
for the group?
3. How should the individual preferences for an item be
merged into a group one?
4. Should the ratings be predicted for each user or for
the group?
5. Who should choose the items to recommend to the
group?
6. How can the recommendations be explained to the
group?
22. Tasks and state of the art survey
1. Types of group
2. Preference acquisition
3. Group modeling
4. Rating prediction
5. Help the members to achieve consensus
6. Explanation of the recommendations
23. 1. Types of group
Tasks and state of the art survey
24. Types of group
¨ Different types of groups lead to different ways in
which the preferences can be modeled [Boratto and
Carta 2011][Carvalho et al. 2013]
¨ A group recommender system can work with:
¤ an established group who share the same long-term interests,
like a group of fans of an artist
¤ an occasional group who has a common specific aim, like
visiting a museum
¤ a random group of people who do not have anything in
common (e.g., the recommendation of background music in a
room)
25. Types of group
Established groups in the literature
¨ PolyLens [O’Connor et al. 2001]
¤ Movie recommendation, considering that people usually
go to the cinema with the same group
¨ GRec_OC (Group Recommender for Online
Communities) [Kim et al. 2010]
¤ Book recommender system for online communities (i.e.,
people with similar interests that share information)
26. Types of group
Occasional groups in the literature
¨ MusicFX [McCarthy and Anagnost 1998]
¤ Music recommendation to people working out in a gym
at a given time
¨ INTRIGUE [Ardissono et al. 2003]
¤ Suggest tourist attractions to groups of users traveling
together
¤ The system can work with subgroups, to weight
differently people with special needs (e.g., children or
disabled people)
27. Types of group
Occasional groups in the literature
¨ [Liu et al. 2012] defines event-based social networks,
i.e., communities of people who attend social events,
by considering both online and offline interactions
28. Types of group
Random groups in the literature
¨ G.A.I.N. [Pizzutilo et al. 2005]
¤ Recommends news to a group of users that are in a
public space at a specific time
¨ FIT (Family Interactive TV System) [Goren-Bar and
Glinansky 2004]
¤ Looks at the probability of each family member to
watch TV in a time slot and predicts who there might be
watching TV
29. Types of group
Random groups in the literature
¨ Flytrap [Crossen et al. 2002] and Jukola [O’Hara et
al. 2004]
¤ Select music to be played in a public room
¤ Flytrap considers the preferences of the users present in
the room at the moment of the song selection
¤ Jukola allows artists to upload their MP3s and those in
the room can express their vote
31. Preference acquisition
¨ A system can acquire explicit or implicit preferences
¨ They can be collected considering that
¤ a user is a part of a group (group preferences),
¤ or not (individual preferences)
¨ Observational studies show that when individual
users interact, their preferences evolve [Delic et al.
2016]
¨ The type of preference acquisition leads to
completely different ways in which information is
handled by the system
32. Preference acquisition
Group preferences in the literature
¨ In CATS [McCarthy et al. 2006] members interact and
express their preferences around a shared device called
“DiamondTouch table-top”
33. Preference acquisition
Group preferences in the literature
¨ In Travel Decision Forum [Jameson 2004] each member of
the group can view and copy the preferences of the other
members
34. Preference acquisition
Group preferences in the literature
¨ In [Gartrell et al. 2010], the system allows both
individual and groups to express preferences (e.g.,
a couple watching a movie together)
¨ In [Chen et al. 2008] it is assumed that both
individuals and subgroups express preferences
35. Preference acquisition
Individual preferences in the literature
¨ CoFeel [Chen and Pu
2013] allows to
express through colors
the emotions given by
a song chosen by the
GroupFun music group
recommender system
36. Preference acquisition
Individual preferences in the literature
¨ MusicFX [McCarthy and Anagnost 1998] lets users
express also negative ratings (range [-2,2])
¨ Adaptive Radio [Chao et al. 2005] focuses only on
negative preferences
¤ To avoid playing music that might be disliked by
anyone
37. Preference acquisition
Theoretical study
¨ [Xie and Lui 2015] consider the fact that
recommender systems work with partial information
¤ Moreover, some users cheat (misbehavior)
¨ What is the minimum number of ratings a product
needs so that one can make a reliable evaluation of
its quality?
¨ Developed theoretical models, validated on Flixter
and Netflix data in the group recommendation
context
38. Preference acquisition
Theoretical study
¨ n’: minimum number of ratings needed to tolerate
the misbehaving users
¨ Pr[n’ ≥ n]: the fraction of movies with a minimum
number of ratings larger than or equal to n
40. Group Modeling
¨ In order to derive a group preference for the items,
group modeling strategies combine the individual
user models
¨ “There is no strategy useful in every context
independently from the environment” [Pizzutilo et al.
2005]
¤ The strategy that best models a group has to be
evaluated in the context in which the group is modeled
41. Group Modeling
¨ This topic has been mainly studied by J. Masthoff
¤ More than 10 years
¤ Most recent work that involves all the strategies is
[Masthoff 2015]
¨ 11 existing strategies
43. Group Modeling Strategies
¨ When presenting each strategy, we will use the
following example:
¤ 3 users (u1, u2, u3)
¤ 10 items (i1,…,i10)
¤ Each element of the table represents a rating (1,…,10)
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
44. 1. Additive Utilitarian
¨ Add individual ratings for each item
¨ Also known as Average Strategy
¤ The ordered ranking of the items for a group is the
same
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group 20 21 21 25 26 28 22 15 14 23
45. 1. Additive Utilitarian
Uses in the literature
¨ Pocket RestaurantFinder [McCarthy 2002] recommends
restaurants to a group of people, by averaging the
individual preferences of the group members on
different types of features (location, cost, cuisine, …)
¨ In [Amer-Yahia et al. 2009], the modeling strategy
averages the individual preferences also taking into
account the disagreement of the group members for an
item
¨ [De Pessemier et al. 2013] illustrate that modeling users
with an average is the best way to model individual
preferences in different contexts
46. 2. Multiplicative Utilitarian
¨ Multiplicate individual ratings for each item
¨ [Masthoff 2011] showed it is the strategy that
works best when selecting a sequence of television
items to suit a group of viewers
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group 280 100 336 540 648 800 270 120 84 420
47. 3. Borda Count
¨ Each item gets a number of points, according to the
position in the list of each user
¤ Least favorite item è 0 points
¤ A point is added for the following item
¤ Same rating to more items è points are distributed
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
48. 3. Borda Count
¨ Each item gets a number of points, according to the
position in the list of each user
¤ Least favorite item è 0 points
¤ A point is added for the following item
¤ Same rating to more items è points are distributed
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
i8 and i9 è Least favorite items for u2
Share the lowest points: (0+1)/2=0.5
49. 3. Borda Count
¨ Each item gets a number of points, according to the
position in the list of each user
¤ Least favorite item è 0 points
¤ A point is added for the following item
¤ Same rating to more items è points are distributed
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 4.5 8 3 8 6 4.5 8 1.5 0 1.5
u2 3.5 7.5 2 6.5 5 7.5 6.5 0.5 0.5 3.5
u3 2.5 0 5 3 6 7.5 1 2.5 4 7.5
Group 10.5 15.5 10 17 17 19.5 15.5 4.5 4.5 12.5
i8 and i9 è Least favorite items for u2
Share the lowest points: (0+1)/2=0.5
50. 3. Borda Count
Uses in the literature
¨ [Masthoff 2011] showed it is one of the strategies
that generates most satisfaction when selecting a
sequence of television items to suit a group of
viewers
¨ TravelWithFriends [De Pessemier et al. 2015] uses it
to rank the top-5 travel destinations to recommend
to a group
51. 4. Copeland Rule
¨ Form of majority voting
¨ Sort the items according to their Copeland index
¤ number of times in which an alternative beats the
others, minus the number of times it loses
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
52. 4. Copeland Rule
¨ Form of majority voting
¨ Sort the items according to their Copeland index
¤ number of times in which an alternative beats the
others, minus the number of times it loses
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Item i2 beats item i1, since both u1 and u2
gave a higher rating to it
54. 4. Copeland Rule
Uses in the literature
¨ The approach proposed in [Felfernig et al. 2012]
proved that a form of majority voting is the most
successful in a requirements negotiation context
55. 5. Plurality Voting
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
¨ Each user votes for her/his favorite option
¨ If more than one alternative needs to be selected,
the items that received the highest number of votes
are selected
56. 5. Plurality Voting
¨ Each user votes for her/his favorite option
¨ If more than one alternative needs to be selected,
the items that received the highest number of votes
are selected
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
User u1 selects items i2, i4, i7
57. 5. Plurality Voting
1 2 3 4 5 6
u1 i2, i4, i7 i4, i7 i5 i1 i3 i8
u2 i2, i6 i4, i7 i5 i1 i3 i8, i9
u3 i6, i10 i10 i10 i10 i3 i9
Group i2, i6 i4, i7 i5 i1 i3 i8, i9
User u1 selects items i2, i4, i7
¨ Each user votes for her/his favorite option
¨ If more than one alternative needs to be selected,
the items that received the highest number of votes
are selected
58. 5. Plurality Voting
Uses in the literature
¨ This strategy was implemented and tested by [Senot
et al. 2010] in the TV domain
59. 6. Approval Voting
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
¨ A point is assigned to all the items a user likes
¤ Suppose that each user votes for all the items with a
rating above a certain threshold (let’s say 5)
60. 6. Approval Voting
¨ A point is assigned to all the items a user likes
¤ Suppose that each user votes for all the items with a
rating above a certain threshold (let’s say 5)
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
61. 6. Approval Voting
¨ A point is assigned to all the items a user likes
¤ Suppose that each user votes for all the items with a
rating above a certain threshold (let’s say 5)
¨ Group rating for an item: sum of the individual votes
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 1 1 1 1 1 1 1 1 1
u2 1 1 1 1 1 1 1 1
u3 1 1 1 1 1 1
Group 2 2 3 3 3 3 2 1 1 3
62. 6. Approval Voting
Uses in the literature
¨ To choose the Web pages to recommend to a
group, Let’s Browse [Lieberman et al. 1999]
evaluates if the page currently considered by the
system matches with the user profile above a
certain threshold and recommends the one with the
highest score
¨ It also proved to be successful in contexts in which
the similarity between the users in a group is high
[Bourke et al. 2011]
63. 7. Least Misery
¨ Group rating: lowest rating expressed for an item
by any of the members of the group
¤ usually adopted to model small groups, to make sure
that every member is satisfied
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group 5 1 6 6 8 8 3 4 3 6
64. 7. Least Misery
Uses in the literature
¨ This strategy is used by PolyLens [O’Connor et al.
2001], in order to produce movie recommendations
that satisfy the small groups handled by the system.
¨ GroupLink [Wei et al. 2016] recommends a set of
activities to a group of users. Each user has to be
recommended a minimum number of activities s/he
enjoys
65. 8. Most Pleasure
¨ Group rating: the highest rating expressed for an
item by a member of the group
¨ This strategy is used by [Quijano-Sanchez et al.
2012] in a system that faces the cold start problem.
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group 8 10 8 10 9 10 10 6 7 10
66. 9. Average without Misery
¨ Group rating: average of the ratings assigned by
each user for that item
¨ The items with a rating under a certain threshold
are not considered (in the example, 4)
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
67. 9. Average without Misery
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group 20 - 21 25 26 28 - 15 - 23
¨ Group rating: average of the ratings assigned by
each user for that item
¨ The items with a rating under a certain threshold
are not considered (in the example, 4)
68. 9. Average without Misery
Uses in the literature
¨ In order to model the preferences of the group for
each genre of music to play in a gym, MusicFX
[McCarthy and Anagnost 1998] sums the individual
ratings expressed by each user, discarding the ones
under a minimum degree of satisfaction.
69. 10. Fairness
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group
¨ Idea: users can be recommended something they do
not like, as long as they also get recommended
something they like
¨ Each user chooses her/his favorite item
¤ Two items with the same rating è choice is based on
the other users
70. 10. Fairness
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group i4
¨ Idea: users can be recommended something they do
not like, as long as they also get recommended
something they like
¨ Each user choose her/his favorite item
¤ Two items with the same rating è choice is based on
the other users
71. 10. Fairness
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group i4 i6
¨ Idea: users can be recommended something they do
not like, as long as they also get recommended
something they like
¨ Each user choose her/his favorite item
¤ Two items with the same rating è choice is based on
the other users
72. 10. Fairness
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group i4 i6 i10
¨ Idea: users can be recommended something they do
not like, as long as they also get recommended
something they like
¨ Each user choose her/his favorite item
¤ Two items with the same rating è choice is based on
the other users
73. 10. Fairness
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group i4 i6 i10 i5
¨ Idea: users can be recommended something they do
not like, as long as they also get recommended
something they like
¨ Each user choose her/his favorite item
¤ Two items with the same rating è choice is based on
the other users
74. 10. Fairness
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group i4 i6 i10 i5 i2 i7 i1 i3 i9 i8
¨ Idea: users can be recommended something they do
not like, as long as they also get recommended
something they like
¨ Each user choose her/his favorite item
¤ Two items with the same rating è choice is based on
the other users
75. 10. Fairness
Uses in the literature
¨ This strategy is adopted by [Christensen and
Schiaffino 2011] in the music recommendation
context
76. 11. Most Respected Person (Dictatorship)
¨ Select the items according to the preferences of the
most respected person
¤ Using the preferences of the others just in case more
than one item received the same evaluation
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group 8 10 7 10 9 8 10 6 3 6
In the example, the most respected person is
u1
77. 11. Most Respected Person (Dictatorship)
Uses in the literature
¨ This strategy is used by INTRIGUE [Ardissono et al. 2003]
that advantages the preferences of a subset of users with
particular needs
¨ G.A.I.N. [Pizzutilo et al. 2005] shows that when people
interact, a user or a small portion of the group influences the
choices of the whole group
¨ In [Jung 2012], long tail users are considered, i.e., an expert
group on a certain attribute. Their ratings are considered to
provide recommendations to the non-expert user group
(short head group)
¨ When the group model of a family is built in [Berkovsky and
Freyne 2010], the person who prepares the recipe has a
higher weight w.r.t. to the partner and the children
79. Rating prediction
¨ Ratings can be predicted using one of the following
3 approaches [Jameson and Smyth 2007]:
1. based on a group model: combine individual
preferences and use it to build predictions for the
group
2. merging recommendations built for the users in a
group
3. aggregating all the predictions built for the users in a
group
80. Rating prediction
Construction of group preference models
¨ Build a group model to combine individual
preferences, then predict a rating for the items that
do not have a score in the group model
¨ Two main steps:
1. Construct a model Mg for a group g (it contains its
preferences)
2. For each item i not rated by the group g, use Mg to
predict a rating pgi
81. Rating prediction
Construction of group preference models
¨ MusicFX [McCarthy and Anagnost 1998] decides the
genre of music to play by randomly selecting one of the
top-m stations available in the group model that
summed the individual preferences
¤ Random to avoid playing the top genre everyday
n The same people might work out at the same time and the same
genre would be played everyday
¨ INTRIGUE [Ardissono et al. 2003] models the
preferences of subgroups of homogeneous people, then
produces the recommendations giving a different
importance to particular categories of people (e.g.,
disabled people)
82. Rating prediction
Construction of group preference models
¨ [Berkovsky and Freyne 2010] showed that when
recommending recipes to a family, a group model
that combines the individual preferences should be
used to make the predictions
¨ To recommend TV programs, TV4M [Yu et al. 2006]
builds a model with the family members who
logged in (i.e., who are in front of the TV)
83. Rating prediction
Merging individual recommendations
¨ Present to a group a set of items, i.e., the merging
of the items with the highest predicted ratings for
each user in the group
¨ The approach works as follows:
1. For each user u in the group:
n For each item i not rated, predict a rating pui
n Select the set Cu of items with the highest predicted
ratings pui
2. Model the preferences of each group by producing
U Cu
84. Rating prediction
Merging individual recommendations
¨ The approach is not widely used in the literature
¨ PolyLens [O’Connor et al. 2001] selects the items
with the highest predicted ratings for each user
¤ Then employs a Least Misery strategy to recommend
the ones with the lowest rating
85. Rating prediction
Merging individual predictions
¨ Predict individual preferences for all the items not
rated by each user, then aggregate individual
preferences for an item into a group model
¨ The approach works as follows:
1. For each item i:
n For each user u who did not rate i, predict a rating pui
n Calculate an aggregate rating rgi from the ratings of the
users in the group
86. Rating prediction
Merging individual predictions
¨ Pocket RestaurantFinder [McCarthy 2002] predicts
a rating for each user and each restaurant and
combines them with an average
¨ Travel Decision Forum [Jameson 2004] builds
predictions for every user (users can copy the
preferences for the others), than predicts a group
score by considering the median of the individual
predictions
87. Rating prediction
Merging individual predictions
¨ E-Tourism [Garcia et al. 2009, Sebastia et al.
2009] build three types of predictions for each
user (demographic, content- and like-based),
aggregates them and selects the group
recommendations from each list
88. 5. Help the members to achieve consensus
Tasks and state of the art survey
89. Help the members to achieve a consensus
¨ Three strategies are usually employed to select the
items to recommend to the group:
1. the system suggests the items with the highest
predicted ratings, without consulting the group;
2. a member of the group is responsible for the final
decision;
3. the users in the group have a conversation, in order to
achieve consensus.
90. Help the members to achieve a consensus
Member responsible for the final decision
¨ Travel Decision Forum [Jameson 2004] allows the
tourist guide to make the final decision
¨ In [Ben-Arieh and Chen 2006], an expert in the
group expresses opinions on an alternative through
linguistic labels (e.g., perfect) and the system
aggregates these labels to make a decision
91. Help the members to achieve a consensus
Conversation between the users
¨ Travel Decision Forum [Jameson 2004] also allows
users to have a conversation
¨ If they’re not in the same room, animated characters
(agents) represent the likely response of the abstent
users
92. 6. Explanation of the recommendations
Tasks and state of the art survey
93. Explanation of the recommendations
¨ The systems deal with preferences of multiple users
¨ Some explain why the proposed items have been
selected for the group
94. Explanation of the recommendations
¨ PolyLens [O’Connor et al. 2001] presents the group
recommendations by showing also the individual
ones
95. Explanation of the recommendations
¨ Let’s Browse
[Lieberman et
al. 1999] shows
the keywords
that led to the
recommendation
96. Explanation of the recommendations
¨ INTRIGUE [Ardissono et al. 2003] gives a long
explanation of why a destination was recommended
to a group
98. Evaluation methods
¨ Three approaches:
1. Offline methods on existing datasets
2. User surveys that that test the effectiveness of a
system by asking users to answer questionnaires
3. Live systems that work in real-world domains, like the
social networks
100. Evaluation methods
Offline methods
¨ No public group recommendation dataset is
available in the literature [Padmanabhan et al.
2011, Quijano-Sanchez et al. 2012]
¤ The partitioning of the users into groups is not available
¨ The vast majority of the approaches adds
constraints on a dataset to infer the groups and
build the recommendations
101. Evaluation methods
User surveys
¨ Users are asked to compile questionnaire to
evaluate the system from several perspectives:
¤ The quality of the recommendations [De Pessemier et
al. 2016]
¤ The usability of the system [Zapata et al. 2015]
102. Evaluation methods
Live systems
¨ GroupLink [Wei et al. 2016] suggests events to
promote group members’ face-to-face interactions in
non-work settings
¨ Identifies and tracking personal preferences by
analyzing individual digital traces (social media,
email, and online streaming histories)
¨ A live system has been developed:
https://bit.ly/group-link
104. Emerging aspects and techniques
1. Advanced recommendation techniques applied to
group recommendation
2. Social group recommender systems
3. Fairness in group recommendations
106. Advanced recommendation techniques
¨ Over the last few years, new recommendation
techniques have been developed to address problems
such as:
¤ sparsity
¤ limited coverage
¨ Two main research directions:
¤ dimensionality reduction
n Compact representation of users and items (most significant
features)
¤ graph-based techniques
n Exploit the transitive relations in the data
¨ They have been recently adopted in group
recommendation problems
108. Advanced recommendation techniques
Graph-based techniques
¨ [Kim and El Saddik 2015] present a stochastic method
¤ Build a bipartite graph and perform random walks to
quantify the influence of nodes (i.e., users and items) and
rank items to recommend to groups
109. Advanced recommendation techniques
Graph-based techniques
¨ COM (COnsensus Model) [Yuan et al. 2014] builds a
generative model that incorporates users’ selection
history and personal considerations of content factors
¨ Users in a group may have different influences (e.g.,
expert in a topic)
111. Social group recommender systems
¨ HappyMovie [Quijano
Sanchez et al. 2014] is
a Facebook application
that recommends
movies to groups
¨ It considers user
preferences, social
interactions, personality
of the users, …
¨ 60 users (35 males and
25 females) tested and
evaluated the
application
113. Fairness in group recommendation
¨ User groups may be heterogeneous, consisting of
people with potentially dissimilar preferences.
¨ If an item is overall good for the group, there could
be one or more members that do not like it
¨ These users would be frustrated if the item is
selected by the group!
¨ Measuring how fair are the items recommended for
a group is central
114. Fairness in group recommendation
¨ [Qi et al. 2016] and [Serbos et al. 2017] study
fairness in the package-to-group
recommendation scenario. The two works
introduce two metrics:
1. m-Proportionality: For a user u, and a package P, P
is m-proportional for u, for m ≥ 1, if there exist at
least m items in P that u likes. For a group of users G,
and a package P, the m-proportionality of the
package P for the group G is defined as: |GP|/|G|
n where GP ⊆ G is the set of users in the group for which
the package P is m-proportional.
115. Fairness in group recommendation
2. m-Envy-Freeness: a user u feels that a package is
fair, if there are m items for which the user is in the
favored top-∆% of the group. Otherwise, the user has
envy against the other members of the group, who
always get a better deal, and thus feels she is being
treated unfairly. For a group of users G, and a
package P, the m-envy-freeness of the package P for
the group G is defined as: |Gef|/|G|
n where Gef ⊆ G is the set of users in the group for which
the package P is m-envy-free.
116. Fairness in group recommendation
¨ [Lin et al. 2017] recommend items to a group, by
ensuring fairness thanks to Pareto efficiency
¨ A solution is called Pareto efficient if none of the
objective functions can be improved without degrading
some of the other objectives.
¨ Several greedy algorithms that optimize different
fairness metrics are proposed and the most effective is
that based on the variance of the ratings of the users:
FVar(g,I) = 1-Var({U(u,I), ∀u∈g}
¨ This last solution outperform the two previous metrics in
terms of accuracy
118. Group recommendation with automatic
detection of groups
¨ Example:
recommendation flyers
¨ Nielsen estimates that
1B Euros per year is
spent to print 12M
flyers
¨ 14.6B Euros are
estimated to be spent
by the customers thanks
to these flyers
http://www.nielsen.com/content/dam/c
orporate/Italy/reports/2012/Le nuove
tendenze del largo consumo (R. de
Camillis).pdf
127. Group recommendation and automatic
detection of groups
¨ Research questions:
1. How should we predict the ratings in this context?
n individual predictions for each user?
n group predictions?
2. How should we group the users for recommendation
purposes?
3. How should we generate group models that contain
the preferences for a group?
128. Group recommendation and automatic
detection of groups
¨ [Boratto and Carta 2015] shows that:
1. Ratings should be predicted for individual users
2. Groups should be detected with a clustering
algorithm (k-means) that also includes the predictions
in the input
3. Groups should be modeled through an average of
the individual ratings (Additive Utilitarian)
n It represents the centroid of the cluster
130. Open issues and research challenges
¨ No public dataset available
¤ With both group structure and individual/group preferences
¨ Evaluation
¤ How effective are the group recommendations? Consider both
individual satisfaction and that of the group as a whole
¨ Explanations with model-based algorithms
¤ Recommendations are based on latent features and explaining
them is challenging
¨ Understanding and employing group dynamics
¤ Integrating the evolution of the individual preferences that
happens because of the group dynamics is still an open issue
¨ Novelty, diversity, and serendipity
¤ Generating novel, diverse, and serendipitous recommendations
for the whole group is challenging
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