This document presents a collaborative recommender system based on k-separability. The system addresses challenges in collaborative filtering like sparsity and noise in user rating data. It uses a dynamic neural network architecture that estimates the optimal number of separable clusters (k) in the data during training. The network is constructed iteratively using a constructive algorithm to add neurons and adapt weights. An experiment evaluated the system on a sparse, noisy dataset and found it produced meaningful recommendations.
Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance.
Explore a use case for vehicle recommendations at mobile.de, Germany’s biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings—providing a holistic solution for candidate generation and ranking.
The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds.
Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance.
Join Marcel Kurovski to explore a use case for vehicle recommendations at mobile.de, Germany’s biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings—providing a holistic solution for candidate generation and ranking.
The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds.
Event: O'Reilly Artificial Intelligence Conference, New York, 18.04.2019
Speaker: Marcel Kurovski, inovex GmbH
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
8 better ways of doing your engineering projecttalkingkarthik
Projects during the course of Engineering can be vital in getting an initial breakthrough into the technical industry besides giving a hands on experience with the technology, which is much valued than the theoretical scholarship. The value that a project adds to one's CV can be boosted in a number of ways. Rather than spending the time on projects that are just "exercises", utilizing it to solve some "real world" technical problem will fetch more value, visibility and understanding of the technology. There are several opportunities like contests, internships etc available for the students to contribute their innovative ideas and gain wide recognition. Affiliation of the projects with such well known programs provides important networking and career openings. Students can also get the maximum out of their projects by converting them into publications of reputed conferences. Developing a project into an entrepreneurial venture is becoming a hot trend these days. Backed by many generous grants, this idea can also be an interesting game to try.
This session throws light on some of the most happening opportunities for the Engineering students and the best practices.
Keynote on software sustainability given at the 2nd Annual Netherlands eScience Symposium, November 2014.
Based on the article
Carole Goble ,
Better Software, Better Research
Issue No.05 - Sept.-Oct. (2014 vol.18)
pp: 4-8
IEEE Computer Society
http://www.computer.org/csdl/mags/ic/2014/05/mic2014050004.pdf
http://doi.ieeecomputersociety.org/10.1109/MIC.2014.88
http://www.software.ac.uk/resources/publications/better-software-better-research
Designing an effective information architectureoptimalworkshop
It’s such a waste when stuff is hard to find. In the book Ambient Findability, Peter Morville quotes a study that estimates that in a medium-sized hospital, 8,000 hours a year of staff time are spent explaining signs and redirecting people. That’s 4 person years!
Finding stuff online is even worse. According to IBM’s chairman, it’s estimated that there will be 44 times as much data and content coming over the next decade, reaching 35 zettabytes by 2020. That’s 35 followed by 21 zeros.
There is one thing you can do to help the madness. You can create an effective information architecture (IA) to connect people with the content that they’re looking for. In this practical workshop you’ll learn how to create an effective IA which will help ensure that your stuff is easy to find and provide your visitors with a great experience. You’ll leave with an armload of practical insights and tips, and with the inspiration to refine and test your own IA.
Immersive Recommendation Workshop, NYC Media Lab'17Longqi Yang
The rapid evolution of deep learning technologies and the explosion of diverse user interaction traces have brought significant challenges and opportunities to recommendation and personalized systems. In this workshop, we discussed recent trends and techniques in user modeling and presented our work on immersive recommendation systems. These systems learn users’ preferences from diverse digital trace modalities (text, image and unstructured data streams) in a wide range of recommendation domains (creative art, food, news, and events). The workshop included a light tutorial on OpenRec, an open source framework that enables quick prototyping of complex recommender systems via modularization.
This workshop is based on research and development done at Cornell Tech as part of the Connected Experiences Lab, supported by Oath and NSF.
This talk introduces Linked Data and Semantic Web by using two examples - population sciences grid and semantAqua - a semantically enabled environmental monitoring. It shows a few tools and the semantic methodology and opens discussion for LOD and team science
Cat Herding and Community Gardens: Practical e-Science Project ManagementNeil Chue Hong
A talk given by Neil Chue Hong at the e-Science Project Management Symposium looking at issues and models of managing projects which are cross-organisation, cross-discipline and cross-usertype, based on experience of managing several e-Science projects.
Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance.
Explore a use case for vehicle recommendations at mobile.de, Germany’s biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings—providing a holistic solution for candidate generation and ranking.
The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds.
Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance.
Join Marcel Kurovski to explore a use case for vehicle recommendations at mobile.de, Germany’s biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings—providing a holistic solution for candidate generation and ranking.
The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds.
Event: O'Reilly Artificial Intelligence Conference, New York, 18.04.2019
Speaker: Marcel Kurovski, inovex GmbH
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
8 better ways of doing your engineering projecttalkingkarthik
Projects during the course of Engineering can be vital in getting an initial breakthrough into the technical industry besides giving a hands on experience with the technology, which is much valued than the theoretical scholarship. The value that a project adds to one's CV can be boosted in a number of ways. Rather than spending the time on projects that are just "exercises", utilizing it to solve some "real world" technical problem will fetch more value, visibility and understanding of the technology. There are several opportunities like contests, internships etc available for the students to contribute their innovative ideas and gain wide recognition. Affiliation of the projects with such well known programs provides important networking and career openings. Students can also get the maximum out of their projects by converting them into publications of reputed conferences. Developing a project into an entrepreneurial venture is becoming a hot trend these days. Backed by many generous grants, this idea can also be an interesting game to try.
This session throws light on some of the most happening opportunities for the Engineering students and the best practices.
Keynote on software sustainability given at the 2nd Annual Netherlands eScience Symposium, November 2014.
Based on the article
Carole Goble ,
Better Software, Better Research
Issue No.05 - Sept.-Oct. (2014 vol.18)
pp: 4-8
IEEE Computer Society
http://www.computer.org/csdl/mags/ic/2014/05/mic2014050004.pdf
http://doi.ieeecomputersociety.org/10.1109/MIC.2014.88
http://www.software.ac.uk/resources/publications/better-software-better-research
Designing an effective information architectureoptimalworkshop
It’s such a waste when stuff is hard to find. In the book Ambient Findability, Peter Morville quotes a study that estimates that in a medium-sized hospital, 8,000 hours a year of staff time are spent explaining signs and redirecting people. That’s 4 person years!
Finding stuff online is even worse. According to IBM’s chairman, it’s estimated that there will be 44 times as much data and content coming over the next decade, reaching 35 zettabytes by 2020. That’s 35 followed by 21 zeros.
There is one thing you can do to help the madness. You can create an effective information architecture (IA) to connect people with the content that they’re looking for. In this practical workshop you’ll learn how to create an effective IA which will help ensure that your stuff is easy to find and provide your visitors with a great experience. You’ll leave with an armload of practical insights and tips, and with the inspiration to refine and test your own IA.
Immersive Recommendation Workshop, NYC Media Lab'17Longqi Yang
The rapid evolution of deep learning technologies and the explosion of diverse user interaction traces have brought significant challenges and opportunities to recommendation and personalized systems. In this workshop, we discussed recent trends and techniques in user modeling and presented our work on immersive recommendation systems. These systems learn users’ preferences from diverse digital trace modalities (text, image and unstructured data streams) in a wide range of recommendation domains (creative art, food, news, and events). The workshop included a light tutorial on OpenRec, an open source framework that enables quick prototyping of complex recommender systems via modularization.
This workshop is based on research and development done at Cornell Tech as part of the Connected Experiences Lab, supported by Oath and NSF.
This talk introduces Linked Data and Semantic Web by using two examples - population sciences grid and semantAqua - a semantically enabled environmental monitoring. It shows a few tools and the semantic methodology and opens discussion for LOD and team science
Cat Herding and Community Gardens: Practical e-Science Project ManagementNeil Chue Hong
A talk given by Neil Chue Hong at the e-Science Project Management Symposium looking at issues and models of managing projects which are cross-organisation, cross-discipline and cross-usertype, based on experience of managing several e-Science projects.
This presentation was provided by Rachel Bruce ofInformation Environment, JISC during the NISO event, "Library Resource Management Systems: New Challenges, New Opportunities," held October 8 - 9, 2009.
Facets and Pivoting for Flexible and Usable Linked Data ExplorationRoberto García
The success of Open Data initiatives has increased the amount of data available on the Web. Unfortunately, most of this data is only available in raw tabular form, what makes analysis and reuse quite difficult for non-experts. Linked Data principles allow for a more sophisticated approach by making explicit both the structure and semantics of the data. However, from the end-user viewpoint, they continue to be monolithic files completely opaque or difficult to explore by making tedious semantic queries. Our objective is to facilitate the user to grasp what kind of entities are in the dataset, how they are interrelated, which are their main properties and values, etc. Rhizomer is a tool for data publishing whose interface provides a set of components borrowed from Information Architecture (IA) that facilitate awareness of the dataset at hand. It automatically generates navigation menus and facets based on the kinds of things in the dataset and how they are described through metadata properties and values. Moreover, motivated by recent tests with end-users, it also provides the possibility to pivot among the faceted views created for each class of resources in the dataset.
FAIR Assessment for Repositories and Researchers EOSCpilot .eu
FAIR Assessment for Repositories and Researchers by Eliane Fankhauser - DANS, delivered during the FAIR Data Session at the EOSC Stakeholders Forum 2018
Presentation at the Bharathi Dasan Institute of Management (BIM), Tiruchirappalli, 2 February 2012.
This slide deck provides a sense of the multi-stakeholder processes that have made the Internet what it is today. The presentation speaks of the support of the multi-stakeholder model, as well as user-centric Internet.
It also mentions that the Internet is a social catalyst to changing the world.
Shortly after this presentation, I was interviewed by the Hindu Newspaper for an article published on 3 February 2012.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Thesis Statement for students diagnonsed withADHD.ppt
k-Separability Presentation
1. An Efficient Collaborative Recommender System
based on k -separability
Georgios Alexandridis Georgios Siolas Andreas Stafylopatis
Department of Electrical and Computer Engineering
National Technical University of Athens
20th International Conference on Artificial Neural Networks
(ICANN 2010)
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 1 / 16
2. Outline
1 Current Trends in Recommender Systems
Recommender Systems
Design Issues
2 Theoretical & Practical Aspects of our Contribution
k-Separability
System Architecture
3 Evaluating our System
Experiment
Results
Conclusions
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 2 / 16
3. What are the Recommender Systems?
Recommender Systems attempt to present information items (e.g.
movies, music, books, news stories) that are likely to be of interest
to the user.
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 3 / 16
4. What are the Recommender Systems?
Recommender Systems attempt to present information items (e.g.
movies, music, books, news stories) that are likely to be of interest
to the user.
Some implementations
Amazon
"Customers Who Bought This Item Also Bought"
Google News
"Recommended Stories"
Online Audio Broadcasters
last.fm
Pandora
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 3 / 16
5. Taxonomy of Recommender Systems
Criterion: How are the predictions made?
Content-Based Recommenders
Locate "similar" items
Collaborative Recommenders
Find "like-minded" users
Hybrid Recommenders
Combination of the two
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 4 / 16
6. Taxonomy of Recommender Systems
Criterion: How are the predictions made?
Content-Based Recommenders
Locate "similar" items
Collaborative Recommenders
Find "like-minded" users
Hybrid Recommenders
Combination of the two
Which method is the best?
Open academic subject
Highly dependent on the application domain
We followed the Collaborative Recommender approach
Computationally simpler than the Hybrid approach
A user rating is more than a mere number; it is an aggregation of
various characteristics
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 4 / 16
7. Collaborative Recommender Systems
Key Component: The User Ratings’ Matrix
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 5 / 16
8. Collaborative Recommender Systems
Key Component: The User Ratings’ Matrix
Ratings
Indicate how much a user likes an item
"like" "dislike"
1-star up to 5-stars
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 5 / 16
9. Collaborative Recommender Systems
Key Component: The User Ratings’ Matrix
Ratings
Indicate how much a user likes an item
"like" "dislike"
1-star up to 5-stars
I1 I2 I3 I4
U1 5 3 2
U2 3 5 2
U3 1 2
U4 2 3
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 5 / 16
10. Collaborative Recommender Systems
Key Component: The User Ratings’ Matrix
Ratings
Indicate how much a user likes an item
"like" "dislike"
1-star up to 5-stars
I1 I2 I3 I4
U1 5 3 2
U2 3 5 2
U3 1 2
U4 2 3
Users become each other’s predictor
By locating positive and negative correlations among them.
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 5 / 16
11. Challanges in Collaborative Recommender System
Design
1 The cold-start problem
2 The sparsity problem
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 6 / 16
12. Challanges in Collaborative Recommender System
Design
1 The cold-start problem
Recommendations cannot be made unless a user has provided
some ratings
Solutions:
Recommend the most popular items
Explicity ask the user to rate some items prior to making
recommendations
2 The sparsity problem
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 6 / 16
13. Challanges in Collaborative Recommender System
Design
1 The cold-start problem
Recommendations cannot be made unless a user has provided
some ratings
Solutions:
Recommend the most popular items
Explicity ask the user to rate some items prior to making
recommendations
2 The sparsity problem
The ratings matrix is sparse
Empty elements: More than 90%
Solution: Dimensionality Reduction techniques
Singular Value Decomposition (SVD) yields good results
Pros: The resultant matrix is substantially smaller & densier
Cons: The dataset becomes very "noisy"
Most elements assume values that are marginally larger than zero
Conclusion: We are in need of techniques that can "learn" noisy
datasets!
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 6 / 16
14. "Noisy" Datasets
The added noise in the dataset hinders the discovery of patterns
in data
Data clusters become difficult to separate
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 7 / 16
15. "Noisy" Datasets
The added noise in the dataset hinders the discovery of patterns
in data
Data clusters become difficult to separate
Machine Learning techniques for highly non-separable datasets
Support Vector Machines, Radial Basis Functions
Evolutionary Algorithms
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 7 / 16
16. "Noisy" Datasets
The added noise in the dataset hinders the discovery of patterns
in data
Data clusters become difficult to separate
Machine Learning techniques for highly non-separable datasets
Support Vector Machines, Radial Basis Functions
Computing the support vector (or estimating the surface . . . ) can be a
computationally intensive task
Evolutionary Algorithms
Meaningful Recommendations are not always guaranteed
(evolutionary dead-ends)
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 7 / 16
17. "Noisy" Datasets
The added noise in the dataset hinders the discovery of patterns
in data
Data clusters become difficult to separate
Machine Learning techniques for highly non-separable datasets
Support Vector Machines, Radial Basis Functions
Computing the support vector (or estimating the surface . . . ) can be a
computationally intensive task
Evolutionary Algorithms
Meaningful Recommendations are not always guaranteed
(evolutionary dead-ends)
Our approach: Use k -separability!
Originally proposed by W. Duch1
Special case of the more general method of Projection Pursuit
Application to Feed-Forward ANNs
Extends linear separability of data clusters into k > 2 segments on
the discriminating hyperplane
1
W. Duch, K-separability. Lecture Notes in Computer Science 4131 (2006) 188-197
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 7 / 16
18. Extending linear separability to 3-separability
The 2-bit XOR problem
A highly non-separable dataset
It can be learned by a 2-layered perceptron, or ...
...by a single layer percpetron that implements k -separability!
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 8 / 16
19. Extending linear separability to 3-separability
The 2-bit XOR problem
A highly non-separable dataset
It can be learned by a 2-layered perceptron, or ...
...by a single layer percpetron that implements k -separability!
The activation function must partition the input space into 3
distinct areas
1.2
1
0.8
0.6
0.4
0.2
0
−0.2
−0.2 0 0.2 0.4 0.6 0.8 1 1.2
(a) Input Space Partitioning
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 8 / 16
20. Extending linear separability to 3-separability
The 2-bit XOR problem
A highly non-separable dataset
It can be learned by a 2-layered perceptron, or ...
...by a single layer percpetron that implements k -separability!
The activation function must partition the input space into 3
distinct areas
Soft-Windowed Activation Functions
1.2
1
1
0.8 0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
−0.2 0
−0.2 0 0.2 0.4 0.6 0.8 1 1.2 −2 −1 0 1 2 3 4
(a) Input Space Partitioning (b) Soft-Windowed Activation
Function
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 8 / 16
21. Generalizing to k -separability
Complex Datasets
Combine the output of two neurons (or more . . . )
e.g. A 5-separable dataset can be learned by the combined output
of 2 neurons
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 9 / 16
22. Generalizing to k -separability
Complex Datasets
Combine the output of two neurons (or more . . . )
e.g. A 5-separable dataset can be learned by the combined output
of 2 neurons
Generalization by Induction
m-neuron output ⇒ 2m + 1 regions on the discriminating line
⇒ k = 2m + 1-separable dataset
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 9 / 16
23. Generalizing to k -separability
Complex Datasets
Combine the output of two neurons (or more . . . )
e.g. A 5-separable dataset can be learned by the combined output
of 2 neurons
Generalization by Induction
m-neuron output ⇒ 2m + 1 regions on the discriminating line
⇒ k = 2m + 1-separable dataset
Use in a Recommendation Engine
Create a 2-layered perceptron
n-sized input vector, m-sized hidden layer, single output layer
Overall, an n → m → 1 projection
Build a model (NN) for each user
Input: The ratings of the n "neighbors" of the target user on an item
he hasn’t evaluated
Output: A "score" for the unseen item
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 9 / 16
24. Implementation Details
The index of separability (k ) is not known a-priori
Setting k to a fixed value is of little help
It can lead to either overspecialization or to large training errors
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 10 / 16
25. Implementation Details
The index of separability (k ) is not known a-priori
Setting k to a fixed value is of little help
It can lead to either overspecialization or to large training errors
Therefore, k is a problem parameter: it has to be estimated
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 10 / 16
26. Implementation Details
The index of separability (k ) is not known a-priori
Setting k to a fixed value is of little help
It can lead to either overspecialization or to large training errors
Therefore, k is a problem parameter: it has to be estimated
Dynamic Network Architecture
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 10 / 16
27. Implementation Details
The index of separability (k ) is not known a-priori
Setting k to a fixed value is of little help
It can lead to either overspecialization or to large training errors
Therefore, k is a problem parameter: it has to be estimated
Dynamic Network Architecture
Sparse user ratings’ matrix ⇒ small overall network size ⇒
Constructive Network Algorithm
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 10 / 16
28. Implementation Details
The index of separability (k ) is not known a-priori
Setting k to a fixed value is of little help
It can lead to either overspecialization or to large training errors
Therefore, k is a problem parameter: it has to be estimated
Dynamic Network Architecture
Sparse user ratings’ matrix ⇒ small overall network size ⇒
Constructive Network Algorithm
Our constructive network algorithm was derived from the New
Constructive Algorithm2
2
Islam MM et al. A new constructive algorithm for architectural and functional adaptation of artificial neural
networks.
IEEE Trans Syst Man Cybern B Cybern. 2009 Dec;39(6):1590-605
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 10 / 16
29. Constructive Network Algorithm
1 Create a minimal architecture
2 Train the network in two phases on the whole Training Set
3 Iteratively add neurons in the hidden layer
Create new Training Sets based on the Classification Error
(Boosting Algorithm)
Only the newly added neuron’s weights are adapted; all other
remain "frozen"
4 Stop network construction when the Classification Error stabilizes
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 11 / 16
30. Constructive Network Algorithm
1 Create a minimal architecture
2 Train the network in two phases on the whole Training Set
3 Iteratively add neurons in the hidden layer
Create new Training Sets based on the Classification Error
(Boosting Algorithm)
Only the newly added neuron’s weights are adapted; all other
remain "frozen"
4 Stop network construction when the Classification Error stabilizes
Boosting Algorithm
Inspired from AdaBoost and used in Network Training as a way of
avoiding local minima
Functionality
Unlearned samples ⇒ New neurons in the hidden layer ⇒ New
clusters discovered
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 11 / 16
31. Our Collaborative Recommender System
Input: The user ratings’ matrix and the target user
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 12 / 16
32. Our Collaborative Recommender System
Input: The user ratings’ matrix and the target user
Output: A model (NN) for the target user
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 12 / 16
33. Our Collaborative Recommender System
Input: The user ratings’ matrix and the target user
Output: A model (NN) for the target user
Steps
1 Pick from the user ratings’ matrix all the co-raters of the target user
2 Compute the SVD of the co-raters matrix, retaining only the
non-zero Singular Values
3 Partition the resultant matrix in 3 different sets; the Training Set, the
Validation Set and the Test Set
4 Train a Constructive ANN Architecture (as discussed previously...)
5 Compute the Performance Metrics on the Test Set
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 12 / 16
34. Experiment
The MovieLens Database
Contains the ratings of 943 users on
1682 movies
Sparse matrix (6.3% of non-zero
elements)
140
Each user has rated at least 20 120
movies (106 on average), but. . . 100
Discrete Exponential Distribution 80
60% of all users have rated 100 60
movies or less 40
40% of all users have rated 50 20
movies or less 0
0 100 200 300 400 500 600 700 800
We followed a purely Collaborative (a) Rated items per user
Strategy
Taking into account only the user
ratings’ and not any other
demographic information
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 13 / 16
35. Experiment
Test Sets & Metrics
Many users rate only a few movies. How would our system
perform?
How would our system perform on the average case?
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 14 / 16
36. Experiment
Test Sets & Metrics
Many users rate only a few movies. How would our system
perform?
Group A: The few raters user group.
Contains all users who have rated 20-50 movies
How would our system perform on the average case?
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 14 / 16
37. Experiment
Test Sets & Metrics
Many users rate only a few movies. How would our system
perform?
Group A: The few raters user group.
Contains all users who have rated 20-50 movies
How would our system perform on the average case?
Group B: The moderate raters user group.
Contains all users who have rated 51-100 movies
May be used in comparisons to other implementations
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 14 / 16
38. Experiment
Test Sets & Metrics
Many users rate only a few movies. How would our system
perform?
Group A: The few raters user group.
Contains all users who have rated 20-50 movies
How would our system perform on the average case?
Group B: The moderate raters user group.
Contains all users who have rated 51-100 movies
May be used in comparisons to other implementations
We randomly picked 20 users from each group (40 users in total).
The results were averaged for each group
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 14 / 16
39. Experiment
Test Sets & Metrics
Many users rate only a few movies. How would our system
perform?
Group A: The few raters user group.
Contains all users who have rated 20-50 movies
How would our system perform on the average case?
Group B: The moderate raters user group.
Contains all users who have rated 51-100 movies
May be used in comparisons to other implementations
We randomly picked 20 users from each group (40 users in total).
The results were averaged for each group
Metrics
1 Precision
2 Recall
3 F-measure
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 14 / 16
40. Results
Table: Performance Results
Methodology Precision Recall F-measure
OurSystem: User Group B (moderate ratings) 75.38% 82.21% 79.37%
OurSystem: User Group A (few ratings) 74.07% 88.86% 78.97%
MovieMagician Clique-based 74% 73% 74%
Movielens 66% 74% 70%
SVD/ANN 67.9% 69.7% 68.8%
MovieMagician Feature-based 61% 75% 67%
MovieMagician Hybrid 73% 56% 63%
Correlation 64.4% 46.8% 54.2%
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 15 / 16
41. Results
Table: Performance Results
Methodology Precision Recall F-measure
OurSystem: User Group B (moderate ratings) 75.38% 82.21% 79.37%
OurSystem: User Group A (few ratings) 74.07% 88.86% 78.97%
MovieMagician Clique-based 74% 73% 74%
Movielens 66% 74% 70%
SVD/ANN 67.9% 69.7% 68.8%
MovieMagician Feature-based 61% 75% 67%
MovieMagician Hybrid 73% 56% 63%
Correlation 64.4% 46.8% 54.2%
Observations
Our system achieves good results in both usergroups and
outperforms the other approaches
Recall is higher in the few raters group because they seem to rate
only the movies they like
Therefore, the recommender cannot generalize
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 15 / 16
42. Conclusions
We have presented a complete Collaborative Recommender
System that is specifically fit for those cases where information is
limited
Our system achieves a good trade-off between Precision and
Recall, a basic requirement for Recommenders
This is due to the fact that k -separability is able to uncover
complex statistical dependencies (positive and negative)
We don’t need to filter the neighborhood of the target user as other
systems do (e.g. by using the Pearson Correlation Formula).
All "neighbors" are considered
Extremely useful in cases of sparse datasets
Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 16 / 16