Talk deals with a practical recommendation sceaario - item-2-item recommendation (similar/related items) with implicit feedback and context. Solution is provided in the factorization framework.
Paper abstract (to appear in ACM digital Library) Item-to-item recommendation - when the most similar items sought to the actual item - is an important recommendation scenario in practical recommender systems. One way to solve this task is to use the similarity between item feature vectors of factorization models. By doing so, one may transfer the well-known accuracy of factorization models observed at the personalized recommendations to the item-to-item case. This paper introduces context-awareness to item similarities in the factorization framework. Two levels of context-aware similarities are defined and applied to two context-aware implicit feedback based factorization methods (iTALS and iTALSx). We investigate the advantages and drawbacks of the approaches on four real life implicit feedback data sets and we characterize the conditions for their application. The results suggest that it is worth using contextual information for item-to-item recommendations in the factorization framework, however, one should carefully select the appropriate method to achieve similar accuracy gain than in the case of the more general item-to-user recommendation scenario.
Context-aware similarities within the factorization framework (CaRR 2013 pres...Balázs Hidasi
This document summarizes research on incorporating context awareness into item-to-item recommendation similarities within a factorization framework. It describes four levels of context-aware similarity calculation and reports on experiments comparing the levels using four datasets. The results showed that context awareness generally improved recommendations but the degree of improvement depended heavily on the method and quality of the contextual information. The most context-sensitive method (elementwise product level 2) showed huge improvements or decreases depending on the context, while other methods showed only minor gains. Future work could explore different contexts, similarity measures, and evaluation approaches.
This document outlines a course on Principles of Micro Economics taught at ANSAL Institute of Technology in Gurgaon, India in 2012. The 3 credit course is facilitated by Dr. Meenal Sharma Jagtap and aims to familiarize students with basic microeconomic concepts and theories to help them apply economic tools to managerial decision making. The course uses various teaching methods like lectures, assignments, group projects and tests. Students are evaluated throughout the term based on assignments, presentations, quizzes, midterm tests and a final exam. The course covers topics like demand and supply, costs and revenues, market forms, income distribution and international economics across 45 sessions.
This document summarizes Chris Ireland's PhD research on understanding and addressing object-relational impedance mismatch. The research introduces a framework for analyzing impedance mismatch with four levels: conceptual, language, schema, and instance. It also presents a process for applying the framework to understand object-relational mapping strategies. Current work involves developing an equivalence technique to better compare object and relational representations based on a reference model of the domain.
Utilizing additional information in factorization methods (research overview,...Balázs Hidasi
Utilizing additional information in factorization methods is an overview of research into context-aware recommender systems using factorization models. It discusses improving factorization methods from early context-aware tensor models like iTALS and iTALSx to a general factorization framework. The research aims to better model implicit feedback, context, and improve scalability using techniques like conjugate gradient descent learning. Future work includes estimating the utility of context dimensions, modeling continuous context variables, and optimizing models with pairwise ranking loss functions.
Context-aware preference modeling with factorizationBalázs Hidasi
- The document outlines Balázs Hidasi's research on context-aware recommendation models using factorization techniques.
- It introduces context-aware algorithms like iTALS and iTALSx that estimate preferences using ALS learning and scale linearly with data.
- Methods for speeding up ALS through approximate solutions like ALS-CG and ALS-CD are described, providing significant speed gains.
- A General Factorization Framework (GFF) is presented that allows experimenting with novel context-aware preference models beyond traditional approaches.
Lessons learnt at building recommendation services at industry scaleDomonkos Tikk
Industry day keynote presentation held at ECIR 2016, Padova. The talk presents algorithmic, technical and business challenges Gravity R&D encountered from building a recommender system vendor company from being a top Netflix Prize contender.
Recommenders on video sharing portals - business and algorithmic aspectsDomonkos Tikk
This talk was given at HTE Medianet 2015 conference on the business ecosystem and its impact and requirement on recommender solutions on user generated content (UGC) video sharing portals (like Youtube, Dailymotion, etc). Besides the intro on Gravity R&D and the business model, we present some information on a real-world case study. We show that larger view counts are directly influence the recommendation quality.
Neighbor methods vs matrix factorization - case studies of real-life recommen...Domonkos Tikk
This talk was given by István Pilászy, co-founder and head of core development at Gravity R&D, at LSRS workshop at Recsys 2015. Messages of the talk: (1) in industry item-2-item (i2i) recommendation is the dominant case, hardly researched by academia; (2) in industry you have typically implicit feedback data; (3) matrix factorization (MF) is good to optimize error metric, but less obvious for top-N and i2i recommendations. (4) item-kNN in most cases outperforms MF for i2i in terms of CTR; (5) Performance heavily depends on the domain and the recommendation scenario.
Context-aware similarities within the factorization framework (CaRR 2013 pres...Balázs Hidasi
This document summarizes research on incorporating context awareness into item-to-item recommendation similarities within a factorization framework. It describes four levels of context-aware similarity calculation and reports on experiments comparing the levels using four datasets. The results showed that context awareness generally improved recommendations but the degree of improvement depended heavily on the method and quality of the contextual information. The most context-sensitive method (elementwise product level 2) showed huge improvements or decreases depending on the context, while other methods showed only minor gains. Future work could explore different contexts, similarity measures, and evaluation approaches.
This document outlines a course on Principles of Micro Economics taught at ANSAL Institute of Technology in Gurgaon, India in 2012. The 3 credit course is facilitated by Dr. Meenal Sharma Jagtap and aims to familiarize students with basic microeconomic concepts and theories to help them apply economic tools to managerial decision making. The course uses various teaching methods like lectures, assignments, group projects and tests. Students are evaluated throughout the term based on assignments, presentations, quizzes, midterm tests and a final exam. The course covers topics like demand and supply, costs and revenues, market forms, income distribution and international economics across 45 sessions.
This document summarizes Chris Ireland's PhD research on understanding and addressing object-relational impedance mismatch. The research introduces a framework for analyzing impedance mismatch with four levels: conceptual, language, schema, and instance. It also presents a process for applying the framework to understand object-relational mapping strategies. Current work involves developing an equivalence technique to better compare object and relational representations based on a reference model of the domain.
Utilizing additional information in factorization methods (research overview,...Balázs Hidasi
Utilizing additional information in factorization methods is an overview of research into context-aware recommender systems using factorization models. It discusses improving factorization methods from early context-aware tensor models like iTALS and iTALSx to a general factorization framework. The research aims to better model implicit feedback, context, and improve scalability using techniques like conjugate gradient descent learning. Future work includes estimating the utility of context dimensions, modeling continuous context variables, and optimizing models with pairwise ranking loss functions.
Context-aware preference modeling with factorizationBalázs Hidasi
- The document outlines Balázs Hidasi's research on context-aware recommendation models using factorization techniques.
- It introduces context-aware algorithms like iTALS and iTALSx that estimate preferences using ALS learning and scale linearly with data.
- Methods for speeding up ALS through approximate solutions like ALS-CG and ALS-CD are described, providing significant speed gains.
- A General Factorization Framework (GFF) is presented that allows experimenting with novel context-aware preference models beyond traditional approaches.
Lessons learnt at building recommendation services at industry scaleDomonkos Tikk
Industry day keynote presentation held at ECIR 2016, Padova. The talk presents algorithmic, technical and business challenges Gravity R&D encountered from building a recommender system vendor company from being a top Netflix Prize contender.
Recommenders on video sharing portals - business and algorithmic aspectsDomonkos Tikk
This talk was given at HTE Medianet 2015 conference on the business ecosystem and its impact and requirement on recommender solutions on user generated content (UGC) video sharing portals (like Youtube, Dailymotion, etc). Besides the intro on Gravity R&D and the business model, we present some information on a real-world case study. We show that larger view counts are directly influence the recommendation quality.
Neighbor methods vs matrix factorization - case studies of real-life recommen...Domonkos Tikk
This talk was given by István Pilászy, co-founder and head of core development at Gravity R&D, at LSRS workshop at Recsys 2015. Messages of the talk: (1) in industry item-2-item (i2i) recommendation is the dominant case, hardly researched by academia; (2) in industry you have typically implicit feedback data; (3) matrix factorization (MF) is good to optimize error metric, but less obvious for top-N and i2i recommendations. (4) item-kNN in most cases outperforms MF for i2i in terms of CTR; (5) Performance heavily depends on the domain and the recommendation scenario.
Challenges Encountered by Scaling Up Recommendation Services at Gravity R&DDomonkos Tikk
This talk was given by Bottyan Németh (Gravity R&D Product Owner & Co-founder) in the industry session at ACM Recsys Conference 2015 in Vienna.
Presentation describes the challenges and solution we encountered by scaling up the recommendation services provided by Gravity.
General factorization framework for context-aware recommendationsDomonkos Tikk
This document proposes a general factorization framework (GFF) for context-aware recommendation that takes a preference model as input and computes latent feature matrices for different context dimensions. GFF allows for easy experimentation with various linear models on both explicit and implicit feedback recommendation tasks involving multiple context dimensions. The document demonstrates GFF's potential by exploring different preference models on a 4-dimensional context problem using real-world implicit feedback datasets, showing that proper preference modeling significantly improves accuracy and previously unused models outperform traditional ones. GFF is also extended to incorporate additional information like item metadata, social networks and session data beyond just context.
This is the slides from conTEXT conference held by SPSS Consulting (Hungary) at 2014.11.21. Slides are in Hungarian
Tartalomgazdagítás szövegbányászattal Linked Open Data alapján és felhasználása ajánlórendszerekben
Ajánlórendszerek jellemzően két adatforrás alapján dolgoznak: a felhasználók tartalomfogyasztási szokásaik (collaborative filtering) és a tartalomak metaadatai alapján (content based filtering). A tartalomleíró adatoknak nagy előnye, hogy az ajánlások könnyen magyarázhatóak a felhasználóknak (azért ajánjuk, mert szereted Brad Pitt filmjeit, stb.), és már új tartalmak esetén is alkalmazhatók. Ezért nyilván nagyon fontos, hogy jó minőségűek és minél gazdagabbak legyenek a tartalmakat leíró metaadatok. Ezek a feltételek azonban gyakran nem teljesülnek, ezért szükség ún. tartalomgazdagításra (content enrichment), hogy jobban jellemezhetőek legyenek a tartalmak. A Gravity alkalmazott megoldás nyilvánosan elérhető adatbázisok (Linked Open Data, LOD) segítségével végzi el a tartalomgazdagítást, mint pl. a Freebase és a DBpedia. A probléma számos szövegbányászati részfeladatot tartalmaz, mint pl. névelemek felismerése és egyértelműsítése (színész, rendező neve; film címe), névelemek tulajdonságainak meghatározása (pl. színész fontosabb adatai) különböző forrásokból származó adatok egyesítése, átfedő, ill. esetleg inkonzisztens adatok kanonizálása. Az előadás során röviden ismertetésre kerül az általunk alkalmazott RDF alapú megoldás, a SPARQL lekérdezőnyelv és alkalmazása, és rámutatunk néhány lehetséges megoldására a szövegbányázati feladatoknak.
Idomaar is an open-source benchmarking framework for recommender systems created by the CrowdRec EU-project. It enables impartial evaluation of recommenders solutions from different aspect: (1) recommendation quality (2) technical aspects (3) business aspects.
Main contributors of Idomaar: Moviri, Gravity RD, Technical University of Delft, Technical University Berlin
The slideshow was presented at ICMA Conference in Helsinki at the "How to Turn Big Data into Dollars" Workshop organized by Gravity R&D,
The presentation reviews the heterogeneity of data sources at classified media, shows the massive size of data available, and give some insights how to use those data for personalization in various scenarios.
Slides from CARR 2012 WS - Enhancing Matrix Factorization Through Initializat...Domonkos Tikk
Executive summary: The paper propose a new method to solve the cold start problem for matrix factorization using metadata. The method works with the realistic implicit feedback scenario. With a smart initialization of the feature matrices better performance values were achieved on several data sets.
Paper abstract: The implicit feedback based recommendation problem—
when only the user history is available but there are no ratings—is a much harder task than the explicit feedback based recommendation problem, due to the inherent uncertainty of the interpretation of such user feedbacks. Still, this practically important recommendation task received less attention and therefore there are only a few common implicit feedback based algorithms and benchmark datasets. This paper focuses on a common matrix factorization method for the implicit problem and investigates if recommendation performance can be improved by appropriate initialization of the feature vectors before training. We present a general initialization framework that preserves the similarity between entities (users/items) when creating the initial feature vectors,
where similarity is defined using e.g. context or metadata information. We demonstrate how the proposed initialization framework can be coupled with MF algorithms. The efficiency of the initialization is evaluated using various context and metadata based similarity concepts on two implicit variants of the MovieLens 10M
dataset and one real life implicit database. It is shown that performance gain can attain 10% improvement in recall@50 and in AUC@50.
Fast ALS-Based Tensor Factorization for Context-Aware Recommendation from Imp...Domonkos Tikk
Executive summary: The paper deals with the implicit feedback problem for recommender systems, and propose a fast context-aware tensor factorization method that can integrate any kind of contextual information.
Paper abstract: Albeit the implicit feedback based recommendation problem—when only the user history is available but there are no ratings—is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-of-the-art algorithms that are efficient on the explicit case cannot be straightforwardly transformed to the implicit case if scalability should be maintained. There are few implicit feedback benchmark datasets, therefore new ideas
are usually experimented on explicit benchmarks. In this paper, we pro-
pose a generic context-aware implicit feedback recommender algorithm,
coined iTALS. iTALSapplies a fast, ALS-basedtensor factorization learn-
ing method that scales linearly with the number of non-zero elements
in the tensor. The method also allows us to incorporate various contex-
tual information into the model while maintaining its computational effi-
ciency. We present two context-aware implementation variants of iTALS.
The first incorporates seasonality and enables to distinguish user behav-
ior in different time intervals. The other views the user history as sequen-
tial information and has the ability to recognize usage pattern typical
to certain group of items, e.g. to automatically tell apart product types
that are typically purchased repetitively or once. Experiments performed
on five implicit datasets (LastFM 1K, Grocery, VoD, and “implicitized”
Netflix and MovieLens 10M) show that by integrating context-aware
information with our factorization framework into the state-of-the-art
implicit recommender algorithm the recommendation quality improves
significantly.
Recommender Systems Evaluation: A 3D Benchmark - presented at RUE 2012 worksh...Domonkos Tikk
Recommender systems add value to vast content resources by matching users with items of interest. In recent years, immense progress has been made in recommendation techniques. The evaluation of these has however not been matched and is threatening to impede the further development of recommender systems. In this paper we propose an approach that addresses this impasse by formulating a novel evaluation concept adopting aspects from recommender systems research and industry. Our model can express the quality of a recommender algorithm from three perspectives, the end consumer (user), the service provider and the vendor (business and technique for both). We review current benchmarking activities and point out their shortcomings, which are addressed by our model. We also explain how our 3D benchmarking framework would apply to a specific use case.
From a toolkit of recommendation algorithms into a real business: the Gravity...Domonkos Tikk
The document discusses the process of transforming recommendation algorithms into a commercial product. It describes how research tools needed to be adapted to meet business requirements like robustness, low response times, and easy integration. User interactions were leveraged instead of ratings to provide recommendations. Testing involved A/B tests to increase revenue and measure key performance indicators. The resulting product was tailored to different industries and incorporated contextual factors and business rules.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Things to Consider When Choosing a Website Developer for your Website | FODUUFODUU
Choosing the right website developer is crucial for your business. This article covers essential factors to consider, including experience, portfolio, technical skills, communication, pricing, reputation & reviews, cost and budget considerations and post-launch support. Make an informed decision to ensure your website meets your business goals.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Challenges Encountered by Scaling Up Recommendation Services at Gravity R&DDomonkos Tikk
This talk was given by Bottyan Németh (Gravity R&D Product Owner & Co-founder) in the industry session at ACM Recsys Conference 2015 in Vienna.
Presentation describes the challenges and solution we encountered by scaling up the recommendation services provided by Gravity.
General factorization framework for context-aware recommendationsDomonkos Tikk
This document proposes a general factorization framework (GFF) for context-aware recommendation that takes a preference model as input and computes latent feature matrices for different context dimensions. GFF allows for easy experimentation with various linear models on both explicit and implicit feedback recommendation tasks involving multiple context dimensions. The document demonstrates GFF's potential by exploring different preference models on a 4-dimensional context problem using real-world implicit feedback datasets, showing that proper preference modeling significantly improves accuracy and previously unused models outperform traditional ones. GFF is also extended to incorporate additional information like item metadata, social networks and session data beyond just context.
This is the slides from conTEXT conference held by SPSS Consulting (Hungary) at 2014.11.21. Slides are in Hungarian
Tartalomgazdagítás szövegbányászattal Linked Open Data alapján és felhasználása ajánlórendszerekben
Ajánlórendszerek jellemzően két adatforrás alapján dolgoznak: a felhasználók tartalomfogyasztási szokásaik (collaborative filtering) és a tartalomak metaadatai alapján (content based filtering). A tartalomleíró adatoknak nagy előnye, hogy az ajánlások könnyen magyarázhatóak a felhasználóknak (azért ajánjuk, mert szereted Brad Pitt filmjeit, stb.), és már új tartalmak esetén is alkalmazhatók. Ezért nyilván nagyon fontos, hogy jó minőségűek és minél gazdagabbak legyenek a tartalmakat leíró metaadatok. Ezek a feltételek azonban gyakran nem teljesülnek, ezért szükség ún. tartalomgazdagításra (content enrichment), hogy jobban jellemezhetőek legyenek a tartalmak. A Gravity alkalmazott megoldás nyilvánosan elérhető adatbázisok (Linked Open Data, LOD) segítségével végzi el a tartalomgazdagítást, mint pl. a Freebase és a DBpedia. A probléma számos szövegbányászati részfeladatot tartalmaz, mint pl. névelemek felismerése és egyértelműsítése (színész, rendező neve; film címe), névelemek tulajdonságainak meghatározása (pl. színész fontosabb adatai) különböző forrásokból származó adatok egyesítése, átfedő, ill. esetleg inkonzisztens adatok kanonizálása. Az előadás során röviden ismertetésre kerül az általunk alkalmazott RDF alapú megoldás, a SPARQL lekérdezőnyelv és alkalmazása, és rámutatunk néhány lehetséges megoldására a szövegbányázati feladatoknak.
Idomaar is an open-source benchmarking framework for recommender systems created by the CrowdRec EU-project. It enables impartial evaluation of recommenders solutions from different aspect: (1) recommendation quality (2) technical aspects (3) business aspects.
Main contributors of Idomaar: Moviri, Gravity RD, Technical University of Delft, Technical University Berlin
The slideshow was presented at ICMA Conference in Helsinki at the "How to Turn Big Data into Dollars" Workshop organized by Gravity R&D,
The presentation reviews the heterogeneity of data sources at classified media, shows the massive size of data available, and give some insights how to use those data for personalization in various scenarios.
Slides from CARR 2012 WS - Enhancing Matrix Factorization Through Initializat...Domonkos Tikk
Executive summary: The paper propose a new method to solve the cold start problem for matrix factorization using metadata. The method works with the realistic implicit feedback scenario. With a smart initialization of the feature matrices better performance values were achieved on several data sets.
Paper abstract: The implicit feedback based recommendation problem—
when only the user history is available but there are no ratings—is a much harder task than the explicit feedback based recommendation problem, due to the inherent uncertainty of the interpretation of such user feedbacks. Still, this practically important recommendation task received less attention and therefore there are only a few common implicit feedback based algorithms and benchmark datasets. This paper focuses on a common matrix factorization method for the implicit problem and investigates if recommendation performance can be improved by appropriate initialization of the feature vectors before training. We present a general initialization framework that preserves the similarity between entities (users/items) when creating the initial feature vectors,
where similarity is defined using e.g. context or metadata information. We demonstrate how the proposed initialization framework can be coupled with MF algorithms. The efficiency of the initialization is evaluated using various context and metadata based similarity concepts on two implicit variants of the MovieLens 10M
dataset and one real life implicit database. It is shown that performance gain can attain 10% improvement in recall@50 and in AUC@50.
Fast ALS-Based Tensor Factorization for Context-Aware Recommendation from Imp...Domonkos Tikk
Executive summary: The paper deals with the implicit feedback problem for recommender systems, and propose a fast context-aware tensor factorization method that can integrate any kind of contextual information.
Paper abstract: Albeit the implicit feedback based recommendation problem—when only the user history is available but there are no ratings—is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-of-the-art algorithms that are efficient on the explicit case cannot be straightforwardly transformed to the implicit case if scalability should be maintained. There are few implicit feedback benchmark datasets, therefore new ideas
are usually experimented on explicit benchmarks. In this paper, we pro-
pose a generic context-aware implicit feedback recommender algorithm,
coined iTALS. iTALSapplies a fast, ALS-basedtensor factorization learn-
ing method that scales linearly with the number of non-zero elements
in the tensor. The method also allows us to incorporate various contex-
tual information into the model while maintaining its computational effi-
ciency. We present two context-aware implementation variants of iTALS.
The first incorporates seasonality and enables to distinguish user behav-
ior in different time intervals. The other views the user history as sequen-
tial information and has the ability to recognize usage pattern typical
to certain group of items, e.g. to automatically tell apart product types
that are typically purchased repetitively or once. Experiments performed
on five implicit datasets (LastFM 1K, Grocery, VoD, and “implicitized”
Netflix and MovieLens 10M) show that by integrating context-aware
information with our factorization framework into the state-of-the-art
implicit recommender algorithm the recommendation quality improves
significantly.
Recommender Systems Evaluation: A 3D Benchmark - presented at RUE 2012 worksh...Domonkos Tikk
Recommender systems add value to vast content resources by matching users with items of interest. In recent years, immense progress has been made in recommendation techniques. The evaluation of these has however not been matched and is threatening to impede the further development of recommender systems. In this paper we propose an approach that addresses this impasse by formulating a novel evaluation concept adopting aspects from recommender systems research and industry. Our model can express the quality of a recommender algorithm from three perspectives, the end consumer (user), the service provider and the vendor (business and technique for both). We review current benchmarking activities and point out their shortcomings, which are addressed by our model. We also explain how our 3D benchmarking framework would apply to a specific use case.
From a toolkit of recommendation algorithms into a real business: the Gravity...Domonkos Tikk
The document discusses the process of transforming recommendation algorithms into a commercial product. It describes how research tools needed to be adapted to meet business requirements like robustness, low response times, and easy integration. User interactions were leveraged instead of ratings to provide recommendations. Testing involved A/B tests to increase revenue and measure key performance indicators. The resulting product was tailored to different industries and incorporated contextual factors and business rules.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
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Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
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* Importance and benefits of vector search
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* Step-by-step implementation guide
* Live demos with code snippets
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
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Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
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We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
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These topics will be covered
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- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
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See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
3. BACKGROUND
Implicit feedback Context
I2I
Factorization
recommendation
This work
4. IMPLICIT FEEDBACK
User preference not coded explicitely in data
E.g. purchase history
Presence preference assumed
Absence ???
Binary „preference” matrix
Zeroes should be considered
Optimize for
Weighted RMSE
Partial ordering (ranking)
5. CONTEXT
Can mean anything
Here: event context
User U has an event
on Item I
while the context is C
E.g.: time, weather, mood of U, freshness of I, etc.
In the experiments:
Seasonality (time of the day or time of the week)
Time period: week / day
6. FACTORIZATION I
Preference data can be organized into matrix
Size of dimensions high
Data is sparse
Approximate this matrix by the product of two low
rank matrices
Each item and user has a feature vector
Predicted preference is the scalar product of the
item
appropriate vectors
user r
T
r Uu Ii
u ,i
Here we optimize for wRMSE (implicit case)
Learning features with ALS
7. FACTORIZATION II.
Context introduced additional context dimension
Matrix tensor (table of records)
Models for preference prediction:
item
Elementwise product model
Weighted scalar product user r
T
ru , i , c 1 (U u I i C c )
Pairwise model
Context dependent user/item bias
T T T
ru , i , c Uu Ii Uu Cc Ii Cc
context
context
item
+ +
user user item r
8. ITEM-TO-ITEM RECOMMENDATIONS
Items similar to the current item
E.g.: user cold start, related items, etc.
Approaches: association rules, similarity between
item consumption vectors, etc.
In the factorization framework:
Similarity between the feature vectors
T
Scalar product: s i , j Ii I j
T
Ii Ij
Cosine similarity: s i , j
Ii 2
Ij
2
9. SCOPE OF THIS PROJECT
Examination wether item similarities can be
improved
using context-aware learning or prediction
compared to the basic feature based solution
Motivation:
If factorization models are used anyways, it would be
good to use them for I2I recommendations as well
Out of scope:
Comparision with other approaches (e.g. association
rules)
10. CONTEXT-AWARE SIMILARITIES: LEVEL1
The form of computing similarity remains
T
si, j Ii Ij Ii
T
Ij
si, j
Ii 2
Ij
2
Similarity is NOT CA
Context-aware learning is used
Assumption: item models will be more accurate
Reasons: during the learning context is modelled separately
Elementwise product model
Context related effects coded in the context feature vector
Pairwise model
Context related biases removed
11. CONTEXT-AWARE SIMILARITIES: LEVEL2
Incorporating the context features
Elementwise product model
Similarities reweighted by the context feature
Assumption: will be sensitive to the quality of the
context
T
si , j ,c
T
1 Ii Cc I j 1 Ii Cc I j
s i , j ,c
Pairwise model Ii 2
Ij
2
Context dependent promotions/demotions for the
participating items
Assumption: minor improvements to the basic similarity
T T T
s i , j ,c Ii Ij Ii Cc Ij Cc
12. CONTEXT-AWARE SIMILARITIES: LEVEL2
NORMALIZATION
Normalization of context vector
Only for cosine similarity
Elementwise product model:
Makes no difference in the ordering
Recommendation in a given context to a given user
Pairwise model
Might affect results
Controls the importance of item promotions/demotions
T T T
Ii Ij Ii Cc Ij Cc
s i , j ,c
Ii Ij Ii 2
Ij
2 2 2
T T T
Ii Ij Ii Cc Ij Cc
s i , j ,c
Ii Ij Ii 2
Cc 2
Ij Cc
2 2 2 2
13. EXPERIMENTS - SETUP
Four implicit dataset
LastFM 1K – music
TV1, TV2 – IPTV
Grocery – online grocery shopping
Context: seasonality
Both with manually and automatically determined time
bands
Evaluation: recommend similar items to the users’
previous item
Recall@20
MAP@20
Coverage@20
15. EXPERIMENTS - CONCLUSION
Context awareness generally helps
Impromevent greatly depends on method and
context quality
All but the elementwise level2 method:
Minor improvements
Tolerant of context quality
Elementwise product level2:
Possibly huge improvements
Or huge decrease in recommendations
Depends on the context/problem
16. (POSSIBLE) FUTURE WORK
Experimentation with different contexts
Different similarity between feature vectors
Predetermination wether context is useful for
User bias
Item bias
Reweighting
Predetermination of context quality
Different evaluation methods
E.g. recommend to session
17. THANKS FOR THE ATTENTION!
For more of my recommender systems related research visit my website:
http://www.hidasi.eu