This document presents a method for hybridizing multiple recommender systems algorithms to optimize for multiple objectives like accuracy, novelty, and diversity. It uses a multi-objective evolutionary algorithm called SPEA2 to find the Pareto optimal frontier of weight combinations for combining the algorithms. This returns not a single best solution but multiple good trade-offs. The system can then choose a solution from the frontier based on its priority for each objective. It evaluates the approach on movie and music recommendation tasks, combining algorithms like SVD, KNN, and content-based methods.
This session demonstrated how to use data, direct mail and the telephone to build and develop close relationships with donors, culminating in millions of dollars in bequests. Through the use of case studies Christiana will share how to uncover and inspire thousands of bequest prospects to leave the ultimate gift to your organisation. Attendees will leave the presentation with a complete bequest strategic plan that they can implement immediately!
Building web and mobile applications is easier now than ever before. Adopt the architecture of building on APIs and cloud services and 80% of your project will be completed before you start. This talk tracks the history of APIs through mashup mayhem, an openness backlash and to a future where regular people will ask for APIs. Immersed in APIs since 2009, Adam DuVander will share stories of API success and failure. As he looks to the future, Adam will share how the hundred year-old principles of an Italian economist are about to make the lives of today’s creative technologists really interesting.
Pareto-Optimal Search-Based Software Engineering (POSBSE): A Literature SurveyAbdel Salam Sayyad
Paper presented at the 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE’13), San Francisco, USA. May 2013.
This session demonstrated how to use data, direct mail and the telephone to build and develop close relationships with donors, culminating in millions of dollars in bequests. Through the use of case studies Christiana will share how to uncover and inspire thousands of bequest prospects to leave the ultimate gift to your organisation. Attendees will leave the presentation with a complete bequest strategic plan that they can implement immediately!
Building web and mobile applications is easier now than ever before. Adopt the architecture of building on APIs and cloud services and 80% of your project will be completed before you start. This talk tracks the history of APIs through mashup mayhem, an openness backlash and to a future where regular people will ask for APIs. Immersed in APIs since 2009, Adam DuVander will share stories of API success and failure. As he looks to the future, Adam will share how the hundred year-old principles of an Italian economist are about to make the lives of today’s creative technologists really interesting.
Pareto-Optimal Search-Based Software Engineering (POSBSE): A Literature SurveyAbdel Salam Sayyad
Paper presented at the 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE’13), San Francisco, USA. May 2013.
Mobile Recommendation Engine
collaborative filtering and content based approach in hybrid manner then Genetic Algorithm for Enhancement of the Recommendation Engine. by this marketers also will get the unique characteristics of the product that must be created and also recommend to the user.
This presentation is about Multiple Classifier System (Ensemble of Classifiers). At first tell about the general idea of decision making, then address reasons and rationales of using Multiple Classifier System, after that concentrate on designing Multiple Classifier System: 1.Create an Ensemble 2.Combining Classifiers.
V Jornadas eMadrid sobre “Educación Digital”. Roberto Centeno, Universidad Na...eMadrid network
V Jornadas eMadrid sobre “Educación Digital”. Roberto Centeno, Universidad Nacional de Educación a Distancia: Mecanismos de reputación en MOOCs. 2015-06-30
XPLODIV: An Exploitation-Exploration Aware Diversification Approach for Recom...Andrea Barraza-Urbina
Recommender Systems (RS) have emerged to guide users in the task of efficiently browsing/exploring a large product space, helping users to quickly identify interesting products. However, suggestions generated with traditional RS usually do not produce diverse results though it has been argued that diversity is a desirable feature. The study of diversity-aware RS has become an important research challenge in recent years, drawing inspiration from diversification solutions for Information Retrieval (IR). However, we argue it is not enough to adapt IR techniques to RS as they do not place the necessary importance to factors such as serendipity, novelty and discovery which are imperative to RS. In this work, we propose a diversification technique for RS that generates a diversified list of results which not only balances the tradeoff between quality (in terms of accuracy) and diversity, but also considers the trade-off between exploitation of the user profile and exploration of novel products. Our experimental evaluation shows that the proposed approach has comparable results to state of the art approaches. Moreover, through control parameters, our approach can be tuned towards more explorative or exploitative recommendations.
On the Measurement of Test Collection ReliabilityJulián Urbano
The reliability of a test collection is proportional to the number of queries it contains. But building a collection with many queries is expensive, so researchers have to find a balance between reliability and cost. Previous work on the measurement of test collection reliability relied on data-based approaches that contemplated random what if scenarios, and provided indicators such as swap rates and Kendall tau correlations. Generalizability Theory was proposed as an alternative founded on analysis of variance that provides reliability indicators based on statistical theory. However, these reliability indicators are hard to interpret in practice, because they do not correspond to well known indicators like Kendall tau correlation. We empirically established these relationships based on data from over 40 TREC collections, thus filling the gap in the practical interpretation of Generalizability Theory. We also review the computation of these indicators, and show that they are extremely dependent on the sample of systems and queries used, so much that the required number of queries to achieve a certain level of reliability can vary in orders of magnitude. We discuss the computation of confidence intervals for these statistics, providing a much more reliable tool to measure test collection reliability. Reflecting upon all these results, we review a wealth of TREC test collections, arguing that they are possibly not as reliable as generally accepted and that the common choice of 50 queries is insufficient even for stable rankings.
QA Fest 2017. Ilari Henrik Aegerter. What is Context- Driven Testing?QAFest
You might have heard of the existence of context-driven testing and the vibrant community engaged in it. In very simple terms, context-driven testing means to look at a problem first and based on its understanding to develop a solution. The seven principles of context-driven testing are:
1. The value of any practice depends on its context.
2. There are good practices in context, but there are no best practices.
3. People, working together, are the most important part of any project's context.
4. Projects unfold over time in ways that are often not predictable.
5. The product is a solution. If the problem isn't solved, the product doesn't work.
6. Good software testing is a challenging intellectual process.
7. Only through judgment and skill, exercised cooperatively throughout the entire project, are we able to do the right things at the right times to effectively test our products.
This session will go into the meaning of the context-driven approach and principles and tries to convince you that context-driven testing is not only a valid approach but also smoothly integrates with agile development practices.
We will talk about testing as a craft and the value of an engaged community of testers and how you can tap into the magic of context-driven testing to become a world-class tester.
The World Wide Web is moving from a Web of hyper-linked documents to a Web of linked data. Thanks to the Semantic Web technological stack and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets connected with each other to form the so called LOD cloud. As of today, we have tons of RDF data available in the Web of Data, but only a few applications really exploit their potential power. The availability of such data is for sure an opportunity to feed personalized information access tools such as recommender systems. We will show how to plug Linked Open Data in a recommendation engine in order to build a new generation of LOD-enabled applications.
(Lecture given @ the 11th Reasoning Web Summer School - Berlin - August 1, 2015)
Mobile Recommendation Engine
collaborative filtering and content based approach in hybrid manner then Genetic Algorithm for Enhancement of the Recommendation Engine. by this marketers also will get the unique characteristics of the product that must be created and also recommend to the user.
This presentation is about Multiple Classifier System (Ensemble of Classifiers). At first tell about the general idea of decision making, then address reasons and rationales of using Multiple Classifier System, after that concentrate on designing Multiple Classifier System: 1.Create an Ensemble 2.Combining Classifiers.
V Jornadas eMadrid sobre “Educación Digital”. Roberto Centeno, Universidad Na...eMadrid network
V Jornadas eMadrid sobre “Educación Digital”. Roberto Centeno, Universidad Nacional de Educación a Distancia: Mecanismos de reputación en MOOCs. 2015-06-30
XPLODIV: An Exploitation-Exploration Aware Diversification Approach for Recom...Andrea Barraza-Urbina
Recommender Systems (RS) have emerged to guide users in the task of efficiently browsing/exploring a large product space, helping users to quickly identify interesting products. However, suggestions generated with traditional RS usually do not produce diverse results though it has been argued that diversity is a desirable feature. The study of diversity-aware RS has become an important research challenge in recent years, drawing inspiration from diversification solutions for Information Retrieval (IR). However, we argue it is not enough to adapt IR techniques to RS as they do not place the necessary importance to factors such as serendipity, novelty and discovery which are imperative to RS. In this work, we propose a diversification technique for RS that generates a diversified list of results which not only balances the tradeoff between quality (in terms of accuracy) and diversity, but also considers the trade-off between exploitation of the user profile and exploration of novel products. Our experimental evaluation shows that the proposed approach has comparable results to state of the art approaches. Moreover, through control parameters, our approach can be tuned towards more explorative or exploitative recommendations.
On the Measurement of Test Collection ReliabilityJulián Urbano
The reliability of a test collection is proportional to the number of queries it contains. But building a collection with many queries is expensive, so researchers have to find a balance between reliability and cost. Previous work on the measurement of test collection reliability relied on data-based approaches that contemplated random what if scenarios, and provided indicators such as swap rates and Kendall tau correlations. Generalizability Theory was proposed as an alternative founded on analysis of variance that provides reliability indicators based on statistical theory. However, these reliability indicators are hard to interpret in practice, because they do not correspond to well known indicators like Kendall tau correlation. We empirically established these relationships based on data from over 40 TREC collections, thus filling the gap in the practical interpretation of Generalizability Theory. We also review the computation of these indicators, and show that they are extremely dependent on the sample of systems and queries used, so much that the required number of queries to achieve a certain level of reliability can vary in orders of magnitude. We discuss the computation of confidence intervals for these statistics, providing a much more reliable tool to measure test collection reliability. Reflecting upon all these results, we review a wealth of TREC test collections, arguing that they are possibly not as reliable as generally accepted and that the common choice of 50 queries is insufficient even for stable rankings.
QA Fest 2017. Ilari Henrik Aegerter. What is Context- Driven Testing?QAFest
You might have heard of the existence of context-driven testing and the vibrant community engaged in it. In very simple terms, context-driven testing means to look at a problem first and based on its understanding to develop a solution. The seven principles of context-driven testing are:
1. The value of any practice depends on its context.
2. There are good practices in context, but there are no best practices.
3. People, working together, are the most important part of any project's context.
4. Projects unfold over time in ways that are often not predictable.
5. The product is a solution. If the problem isn't solved, the product doesn't work.
6. Good software testing is a challenging intellectual process.
7. Only through judgment and skill, exercised cooperatively throughout the entire project, are we able to do the right things at the right times to effectively test our products.
This session will go into the meaning of the context-driven approach and principles and tries to convince you that context-driven testing is not only a valid approach but also smoothly integrates with agile development practices.
We will talk about testing as a craft and the value of an engaged community of testers and how you can tap into the magic of context-driven testing to become a world-class tester.
The World Wide Web is moving from a Web of hyper-linked documents to a Web of linked data. Thanks to the Semantic Web technological stack and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets connected with each other to form the so called LOD cloud. As of today, we have tons of RDF data available in the Web of Data, but only a few applications really exploit their potential power. The availability of such data is for sure an opportunity to feed personalized information access tools such as recommender systems. We will show how to plug Linked Open Data in a recommendation engine in order to build a new generation of LOD-enabled applications.
(Lecture given @ the 11th Reasoning Web Summer School - Berlin - August 1, 2015)
Presentation given at the Workshop on Recommendation Utility Evaluation: Beyond RMSE in conjunction with the conference on recommender systems (ACM) on September 9, 2012
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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
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.
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/
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
1. Pareto-Efficient Hybridization for
Multi-Objective Recommender
Systems
Marco Tulio Ribeiro 1,2 Anisio Lacerda 1,2
Adriano Veloso 1 Nivio Ziviani 1,2
1 2
Universidade Federal de Minas Gerais Zunnit Technologies
Computer Science Department Belo Horizonte, Brazil
Belo Horizonte, Brazil
ACM Recommender Systems 2012, Dublin, Ireland
September 10th, 2012
1
5. Pareto Efficient Hybridization for
Multi-Objective Recommender Systems
Multi-Objective:
Accuracy
Novelty
Diversity
Hybridization:
Different algorithms have different strengths
2
6. Pareto Efficient Hybridization for
Multi-Objective Recommender Systems
Multi-Objective:
Accuracy
Novelty
Diversity
Hybridization:
Different algorithms have different strengths
Pareto Efficient:
In a moment
2
7. What’s a Good Recommendation?
“Good” is a multifaceted concept
3
8. What’s a Good Recommendation?
“Good” is a multifaceted concept
Are novel recommendations good
recommendations?
3
11. What’s a Good Recommendation?
“Good” is a multifaceted concept
Are novel recommendations good
recommendations?
Are accurate recommendations good
recommendations?
3
14. What’s a Good Recommendation?
“Good” is a multifaceted concept
Are novel recommendations good
recommendations?
Are accurate recommendations good
recommendations?
Are diverse recommendations good
recommendations?
3
17. Our Work
The challenge:
Combining multiple algorithms
4
18. Our Work
The challenge:
Combining multiple algorithms
Contributions:
Domain and algorithm-independent hybrid
4
19. Our Work
The challenge:
Combining multiple algorithms
Contributions:
Domain and algorithm-independent hybrid
Multi-objective in terms of accuracy, novelty
and diversity.
4
20. Our Work
The challenge:
Combining multiple algorithms
Contributions:
Domain and algorithm-independent hybrid
Multi-objective in terms of accuracy, novelty
and diversity.
Adjustable compromise
4
21. Weighted Aggregation
Combine the algorithms using standard
weighted aggregation
5
22. Weighted Aggregation
Combine the algorithms using standard
weighted aggregation
Problem: finding the vector of weights W
5
23. Weighted Aggregation
Combine the algorithms using standard
weighted aggregation
Problem: finding the vector of weights W
Example:
W = [SVD: 2.3, TopPop: −5, ItemKNN : 1]
5
24. Weighted Aggregation
Combine the algorithms using standard
weighted aggregation
Problem: finding the vector of weights W
Example:
W = [SVD: 2.3, TopPop: −5, ItemKNN : 1]
Easy to add or remove algorithms
5
26. Evolutionary Algorithms
A population is created with a group of
random individuals
For each generation:
The individuals of the population are
evaluated (cross validation)
The best individuals are combined, mutated
or kept
6
27. Evolutionary Algorithms
A population is created with a group of
random individuals
For each generation:
The individuals of the population are
evaluated (cross validation)
The best individuals are combined, mutated
or kept
Good for search spaces where little is
known
6
28. Evolutionary Algorithms
A population is created with a group of
random individuals
For each generation:
The individuals of the population are
evaluated (cross validation)
The best individuals are combined, mutated
or kept
Good for search spaces where little is
known
Domain and algorithm-independent
6
39. SPEA2
Strength Pareto Evolutionary Algorithm
[Zitzler, Laumanns and Thiele]
Multi-Objective Evolutionary Algorithm
Uses the Pareto Dominance concept
Returns a Pareto Frontier
O(M 2logM ), but performed offline
7
40. Adjusting the System Priority
The recommender system may desire to
adjust the compromise
8
41. Adjusting the System Priority
The recommender system may desire to
adjust the compromise
We do not return a single solution, but the
Pareto Frontier
8
42. Adjusting the System Priority
The recommender system may desire to
adjust the compromise
We do not return a single solution, but the
Pareto Frontier
Given the priority of each objective, we
choose one individual from the frontier
8
45. Evaluation Methodology
Task: Top-N Item Recommendation
Evaluation methodology similar to
[Cremonesi, Koren and Turrin, RecSys 2010]
9
46. Evaluation Methodology
Task: Top-N Item Recommendation
Evaluation methodology similar to
[Cremonesi, Koren and Turrin, RecSys 2010]
With novelty and diversity from
[Vargas and Castells, RecSys 2011]
9
47. Datasets
Movielens Last.fm
Recommends movies music
Users 6,040 992
Content 3,883 movies 176,948 artists
Ratings/Feedback 1,000,209 19,150,868
Feedback explicit implicit
Table: Summary of Datasets
10
48. Recommendation Algorithms
PureSVD (50 and 150 factors)
[Cremonesi, Koren and Turrin, RecSys 2010]
11
49. Recommendation Algorithms
PureSVD (50 and 150 factors)
[Cremonesi, Koren and Turrin, RecSys 2010]
KNNs: Item and User-based
11
50. Recommendation Algorithms
PureSVD (50 and 150 factors)
[Cremonesi, Koren and Turrin, RecSys 2010]
KNNs: Item and User-based
Most Popular
11
51. Recommendation Algorithms
PureSVD (50 and 150 factors)
[Cremonesi, Koren and Turrin, RecSys 2010]
KNNs: Item and User-based
Most Popular
WRMF
[Hu et al, ICDM 2008, Pan et al ICDM 2008]
11
52. Recommendation Algorithms
PureSVD (50 and 150 factors)
[Cremonesi, Koren and Turrin, RecSys 2010]
KNNs: Item and User-based
Most Popular
WRMF
[Hu et al, ICDM 2008, Pan et al ICDM 2008]
Content-based:
Item Attribute KNN (movielens only)
User Attribute KNN
11
64. Conclusions
A multi-objective hybridization technique for
combining recommendation algorithms
21
65. Conclusions
A multi-objective hybridization technique for
combining recommendation algorithms
“Tune” the system to different priority needs
21
66. Conclusions
A multi-objective hybridization technique for
combining recommendation algorithms
“Tune” the system to different priority needs
Highly reproducible experiments:
Public datasets
Open-source implementations
(MyMediaLite, DEAP)
21
67. Conclusions
A multi-objective hybridization technique for
combining recommendation algorithms
“Tune” the system to different priority needs
Highly reproducible experiments:
Public datasets
Open-source implementations
(MyMediaLite, DEAP)
Competitive with the best algorithms
according to each objective
21
68. Future Work
Test these assumptions using online
AB-testing, in real world E-commerce
websites
22
69. Future Work
Test these assumptions using online
AB-testing, in real world E-commerce
websites
Try maximizing other objectives:
profit, stock diversity, etc
22
70. Future Work
Test these assumptions using online
AB-testing, in real world E-commerce
websites
Try maximizing other objectives:
profit, stock diversity, etc
Figuring out how often the weights need to
be re-adjusted
22
71. Pareto-Efficient Hybridization for
Multi-Objective Recommender
Systems
Marco Tulio Ribeiro 1,2 Anisio Lacerda 1,2
Adriano Veloso 1 Nivio Ziviani 1,2
1 2
Universidade Federal de Minas Gerais Zunnit Technologies
Computer Science Department Belo Horizonte, Brazil
Belo Horizonte, Brazil
ACM Recommender Systems 2012, Dublin, Ireland
September 10th, 2012
23