The document describes a new approach called SPrank for top-N recommendations from implicit feedback using linked open data. SPrank analyzes relationships between user preferences and items through path-based features extracted from a knowledge graph. A learning to rank method is used to learn the ranking function from these features. Experimental results on movie and music datasets mapped to DBpedia show SPrank outperforms other recommendation techniques, particularly with smaller user profiles.
E&P organizations are turning more attention to accumulated data to enhance operating efficiency, safety, and recovery. The computing paradigm is shifting, the O&G paradigm is shifting, and the rise of the machine learning paradigm requires careful attention to top-down integrated systems engineering. A system approach will be presented to stimulate out-of-the-box thinking to address the machine learning paradigm.
Basic introduction to recommender systems + Implementing a content-based recommender system by leveraging knowledge encoded into Linked Open Data datasets
Efficient O&G does not suffice in an industry downturn – effective investment in time and effort is required to rise above the pack
Production analysis need not be mystical; it should not be rote
Nuance and subtle variations provide leading indicators into impending production issues
Decline curves, certainly crucial, must be analyzed in context
Case-based, topological analysis, rule inference, curve plotting solutions are common solutions, but fall short
Application of nuance analysis within environment of Data-Intensive Scientific Discovery
TUW-ASE Summer 2015 - Quality of Result-aware data analyticsHong-Linh Truong
This is a lecture from the advanced service engineering course from the Vienna University of Technology. See http://dsg.tuwien.ac.at/teaching/courses/ase
Machine Learning encompasses data acquisition, transmission, retention, analysis, and reduction. The expected outgrowth of 24x7 data systems and operations centers is Knowledge Engineering and Data Intensive Analytics AKA Machine Learning. This presentation will develop and apply Machine Learning concepts to the Upstream O&G industry. Specific focus will be given to the fundamental concepts and definitions of Machine Learning along with the application of Machine Learning.
E&P organizations are turning more attention to accumulated data to enhance operating efficiency, safety, and recovery. The computing paradigm is shifting, the O&G paradigm is shifting, and the rise of the machine learning paradigm requires careful attention to top-down integrated systems engineering. A system approach will be presented to stimulate out-of-the-box thinking to address the machine learning paradigm.
Basic introduction to recommender systems + Implementing a content-based recommender system by leveraging knowledge encoded into Linked Open Data datasets
Efficient O&G does not suffice in an industry downturn – effective investment in time and effort is required to rise above the pack
Production analysis need not be mystical; it should not be rote
Nuance and subtle variations provide leading indicators into impending production issues
Decline curves, certainly crucial, must be analyzed in context
Case-based, topological analysis, rule inference, curve plotting solutions are common solutions, but fall short
Application of nuance analysis within environment of Data-Intensive Scientific Discovery
TUW-ASE Summer 2015 - Quality of Result-aware data analyticsHong-Linh Truong
This is a lecture from the advanced service engineering course from the Vienna University of Technology. See http://dsg.tuwien.ac.at/teaching/courses/ase
Machine Learning encompasses data acquisition, transmission, retention, analysis, and reduction. The expected outgrowth of 24x7 data systems and operations centers is Knowledge Engineering and Data Intensive Analytics AKA Machine Learning. This presentation will develop and apply Machine Learning concepts to the Upstream O&G industry. Specific focus will be given to the fundamental concepts and definitions of Machine Learning along with the application of Machine Learning.
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)
Paul Groth: Data Analysis in a Changing Discourse: The Challenges of Scholarl...COST Action TD1210
Paul Groth (Elsevier) “Data Analysis in a Changing Discourse: The Challenges of Scholarly Communication“
Presentation at the KnoweScape workshop "Evolution and variation of classification systems" March 4-5, 2015 Amsterdam
Ontologies for Emergency & Disaster Management Stephane Fellah
Ogc meeting march 2014
OGC OWS-10 Cross-Community Interoperability
Ontologies for Emergency & Disaster Management
(The application of geospatial linked data)
Intelligent Software Engineering: Synergy between AI and Software Engineering...Tao Xie
2018 Distinguished Speaker, the UC Irvine Institute for Software Research (ISR) Distinguished Speaker Series 2018-2019. "Intelligent Software Engineering: Synergy between AI and Software Engineering" http://isr.uci.edu/content/isr-distinguished-speaker-series-2018-2019
Ontologies for Crisis Management: A Review of State of the Art in Ontology De...streamspotter
Shuangyan Liu's presentation on "Ontologies for Crisis Management: A Review of State of the Art in Ontology Design and Usability" at ISCRAM 2013 in Baden-Baden.
Smart Canvas @ Large Scale Recommender Systems Workshop 2015Gilmar Souza
These are the slides that myself and Gabriel Moreira (https://br.linkedin.com/in/gabrielspmoreira) used to present the Smart Canvas (www.ciandt.com/smartcanvas) at the Large-Scale Recommender Systems Workshop (https://dato.com/events/lsrs15.html) in Vienna, Austria on September 20th 2015.
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)
Paul Groth: Data Analysis in a Changing Discourse: The Challenges of Scholarl...COST Action TD1210
Paul Groth (Elsevier) “Data Analysis in a Changing Discourse: The Challenges of Scholarly Communication“
Presentation at the KnoweScape workshop "Evolution and variation of classification systems" March 4-5, 2015 Amsterdam
Ontologies for Emergency & Disaster Management Stephane Fellah
Ogc meeting march 2014
OGC OWS-10 Cross-Community Interoperability
Ontologies for Emergency & Disaster Management
(The application of geospatial linked data)
Intelligent Software Engineering: Synergy between AI and Software Engineering...Tao Xie
2018 Distinguished Speaker, the UC Irvine Institute for Software Research (ISR) Distinguished Speaker Series 2018-2019. "Intelligent Software Engineering: Synergy between AI and Software Engineering" http://isr.uci.edu/content/isr-distinguished-speaker-series-2018-2019
Ontologies for Crisis Management: A Review of State of the Art in Ontology De...streamspotter
Shuangyan Liu's presentation on "Ontologies for Crisis Management: A Review of State of the Art in Ontology Design and Usability" at ISCRAM 2013 in Baden-Baden.
Smart Canvas @ Large Scale Recommender Systems Workshop 2015Gilmar Souza
These are the slides that myself and Gabriel Moreira (https://br.linkedin.com/in/gabrielspmoreira) used to present the Smart Canvas (www.ciandt.com/smartcanvas) at the Large-Scale Recommender Systems Workshop (https://dato.com/events/lsrs15.html) in Vienna, Austria on September 20th 2015.
Algorithmic Music Recommendations at SpotifyChris Johnson
In this presentation I introduce various Machine Learning methods that we utilize for music recommendations and discovery at Spotify. Specifically, I focus on Implicit Matrix Factorization for Collaborative Filtering, how to implement a small scale version using python, numpy, and scipy, as well as how to scale up to 20 Million users and 24 Million songs using Hadoop and Spark.
From the NYC Machine Learning meetup on Jan 17, 2013: http://www.meetup.com/NYC-Machine-Learning/events/97871782/
Video is available here: http://vimeo.com/57900625
Interactive Recommender Systems with Netflix and SpotifyChris Johnson
Interactive recommender systems enable the user to steer the received recommendations in the desired direction through explicit interaction with the system. In the larger ecosystem of recommender systems used on a website, it is positioned between a lean-back recommendation experience and an active search for a specific piece of content. Besides this aspect, we will discuss several parts that are especially important for interactive recommender systems, including the following: design of the user interface and its tight integration with the algorithm in the back-end; computational efficiency of the recommender algorithm; as well as choosing the right balance between exploiting the feedback from the user as to provide relevant recommendations, and enabling the user to explore the catalog and steer the recommendations in the desired direction.
In particular, we will explore the field of interactive video and music recommendations and their application at Netflix and Spotify. We outline some of the user-experiences built, and discuss the approaches followed to tackle the various aspects of interactive recommendations. We present our insights from user studies and A/B tests.
The tutorial targets researchers and practitioners in the field of recommender systems, and will give the participants a unique opportunity to learn about the various aspects of interactive recommender systems in the video and music domain. The tutorial assumes familiarity with the common methods of recommender systems.
Learning to Rank for Recommender Systems - ACM RecSys 2013 tutorialAlexandros Karatzoglou
The slides from the Learning to Rank for Recommender Systems tutorial given at ACM RecSys 2013 in Hong Kong by Alexandros Karatzoglou, Linas Baltrunas and Yue Shi.
Recommenders Systems tutorial slides from the European Summer School of Information Retrieval (ESSIR).
Covers basic ideas on Collaborative Filtering, Content-based methods, Matrix Factorization, Restricted Boltzmann Machines, Ranking, Diversity.
The slides include material from Xavier Amatriain, Saul Vargas and Linas Baltrunas.
Using a Reputation Framework to Identify Community Leaders in Ontology Engine...Christophe Debruyne
Using a Reputation Framework to Identify Community Leaders in Ontology Engineering
C. Debruyne and N. Nijs
LNCS 8185, p. 677 ff.
Presented at ODBASE 2013, part of On the Move to Meaningful Internet Systems: OTM 2013 Conferences
“Semantic Technologies for Smart Services” diannepatricia
Rudi Studer, Full Professor in Applied Informatics at the Karlsruhe Institute of Technology (KIT), Institute AIFB, presentation “Semantic Technologies for Smart Services” as part of the Cognitive Systems Institute Speaker Series, December 15, 2016.
A presentation conducted by Dr Amineh Ghorbani, Faculty of Technology, Policy and Management, Delft University of Technology.
Presented on Tuesday the 1st of October 2013.
Infrastructure systems consist of many heterogeneous decision making entities and technological artefacts. They are governed through public policy that unravels in a multi-scale institutional context, ranging from norms and values to technical standards. For example, to integrate biogas infrastructure in a region, various forms of governance, laws and regulations need to be implemented. To effectively design these requirements, insights into socio-technical systems can be gained through agent based modelling and simulation.
To implement such social concepts in agent-based models of infrastructure systems, we designed a modelling framework called MAIA, based on the Institutional Analysis and Development framework of Elinor Ostrom. This paper will explain how MAIA can be used to model a biogas energy infrastructure in the Netherlands.
CPaaS.io Y1 Review Meeting - Holistic Data ManagementStephan Haller
Data management and governance aspects of the CPaaS.io platform as presented at the first year review meeting in Tokyo on October 5, 2017.
Disclaimer:
This document has been produced in the context of the CPaaS.io project which is jointly funded by the European Commission (grant agreement n° 723076) and NICT from Japan (management number 18302). All information provided in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. For the avoidance of all doubts, the European Commission and NICT have no liability in respect of this document, which is merely representing the view of the project consortium. This document is subject to change without notice.
This presentation shows reco4j features and vision. In particular we add the new concept of context aware recommendation and how we integrate it into reco4j. In this new presentation there is also some piece of code that show how simple is integrate our software. See the project site for more details here: http://www.reco4j.org
BUILDING A SCALABLE MULTIMEDIA WEB OBSERVATORYJonathon Hare
Web and Internet Science research group seminar series. University of Southampton. 13th March 2013.
The web is inherently multimedia in nature, and contains data and information in many different audio, visual and textual forms. To fully understand the nature of the web and the information contained within it, it is necessary to harness all modalities of data. Within the EU funded ARCOMEM project, we are building a platform for crawling and analysing samples of web and social-web data at scale. Whilst the project is ostensibly about issues related to intelligent web-archiving, the ARCOMEM software has features that make it ideal for use as a platform for a scalable Multimedia Web Observatory.
This talk will describe the ARCOMEM approach from data harvesting through to detailed content analysis and demonstrate how this approach relates to a multimedia web observatory. In addition to describing the overall framework, I'll show some of the research aspects of the system related specifically to multimodal multimedia data in small (>100GB) to medium-scale (multi-terabyte) web archives, and demonstrate how these are targeted to our Parliamentarian and Journalist end-users.
Nesta palestra no evento GDG DataFest, apresentei uma introdução prática sobre as principais técnicas de sistemas de recomendação, incluindo arquiteturas recentes baseadas em Deep Learning. Foram apresentados exemplos utilizando Python, TensorFlow e Google ML Engine, e fornecidos datasets para exercitarmos um cenário de recomendação de artigos e notícias.
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
Similar to Top-N Recommendations from Implicit Feedback leveraging Linked Open Data (20)
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
CDSCO and Phamacovigilance {Regulatory body in India}NEHA GUPTA
The Central Drugs Standard Control Organization (CDSCO) is India's national regulatory body for pharmaceuticals and medical devices. Operating under the Directorate General of Health Services, Ministry of Health & Family Welfare, Government of India, the CDSCO is responsible for approving new drugs, conducting clinical trials, setting standards for drugs, controlling the quality of imported drugs, and coordinating the activities of State Drug Control Organizations by providing expert advice.
Pharmacovigilance, on the other hand, is the science and activities related to the detection, assessment, understanding, and prevention of adverse effects or any other drug-related problems. The primary aim of pharmacovigilance is to ensure the safety and efficacy of medicines, thereby protecting public health.
In India, pharmacovigilance activities are monitored by the Pharmacovigilance Programme of India (PvPI), which works closely with CDSCO to collect, analyze, and act upon data regarding adverse drug reactions (ADRs). Together, they play a critical role in ensuring that the benefits of drugs outweigh their risks, maintaining high standards of patient safety, and promoting the rational use of medicines.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Adv. biopharm. APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMSAkankshaAshtankar
MIP 201T & MPH 202T
ADVANCED BIOPHARMACEUTICS & PHARMACOKINETICS : UNIT 5
APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMS By - AKANKSHA ASHTANKAR
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Colonic and anorectal physiology with surgical implications
Top-N Recommendations from Implicit Feedback leveraging Linked Open Data
1. Top-N Recommendations
from Implicit Feedback
leveraging Linked Open Data
Vito Claudio Ostuni, Tommaso Di Noia, Eugenio Di Sciascio, Roberto Mirizzi
ostuni@deemail.poliba.it, t.dinoia@poliba.it, disciascio@poliba.it, mirizzi@deemail.poliba.it
Polytechnic University of Bari - Bari (ITALY)
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
2. Outline
Introduction and motivation
SPrank: Semantic Path-based ranking
Data model and Problem formulation
Path-based features
Learning the ranking function
Experimental Evaluation
Contributions and Conclusion
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
3. Linked Open Data
• Initiative for publishing and connecting data on the Web using
Semantic Web technologies;
• >30 billion of RDF triples from hundreds of data sources;
• Semantic Web done right [ http://www.w3.org/2008/Talks/0617-lod-tbl/#(3) ]
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
4. Linked Open Data
• Initiative for publishing and connecting data on the Web using
Semantic Web technologies;
• >30 billion of RDF triples from hundreds of data sources;
• Semantic Web done right [ http://www.w3.org/2008/Talks/0617-lod-tbl/#(3) ]
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
5. Hong Kong in DBpedia
db:Hong_Kong
db:thumbnail
subject
predicate
object
8134 triples
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
6. Hong Kong in DBpedia
db:Hong_Kong
db:thumbnail
Skyscrapers over 350 meters in Hong Kong?
select * where {
?s dbpedia-owl:location <http://dbpedia.org/resource/Hong_Kong>.
?s dcterms:subject
category:Skyscrapers_over_350_meters. }
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
7. Hong Kong in DBpedia
db:Hong_Kong
db:thumbnail
db:location
db:International_Commerce_centre
db:thumbnail
db:Central_Plaza_(Hong_Kong)
dcterms:subject
db:thumbnail
db:category:Skyscrapers_over_350_meters)
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
8. Motivation
Traditional Ontological/Semantic Recommender Systems:
• make use of limited domain ontologies;
• rely on explicit feedback data;
• address the rating prediction task.
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
9. Motivation
Traditional Ontological/Semantic Recommender Systems:
• make use of limited domain ontologies;
• rely on explicit feedback data;
• address the rating prediction task.
But…
• a lot of structured semantic data on the Web;
• Implicit feedback are easier to collect;
• Top-N Recommendations is a more realistic task.
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
10. Motivation
Traditional Ontological/Semantic Recommender Systems:
• make use of limited domain ontologies;
• rely on explicit feedback data;
• address the rating prediction task.
But…
• a lot of structured semantic data on the Web;
• Implicit feedback are easier to collect;
• Top-N Recommendations is a more realistic task.
Challenge:
• compute Top-N Item Recommendations from implicit feedback
exploiting the Web of Data.
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
11. Our approach
• Usage of structured semantic data freely available on the Web
(Linked Open Data) to describe items
DBpedia ontology
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
12. Our approach
• Analysis of complex relations between the user preferences and
the target item (extraction of path-based features)
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
13. Our approach
• Analysis of complex relations between the user preferences and
the target item (extraction of path-based features)
• Formalization of the Top-N Item recommendation problem from
implicit feedback in a Learning To Rank setting
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
14. Data model
Implicit Feedback Matrix
^
S
I1
i2
i3
i4
u1
1
1
0
0
u2
1
0
1
0
u3
0
1
1
0
u4
0
1
0
Knowledge Graph
1
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
15. Data model
Implicit Feedback Matrix
^
S
I1
i2
i3
i4
u1
1
1
0
0
u2
1
0
1
0
u3
0
1
1
0
u4
0
1
0
Knowledge Graph
1
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
16. Data model
Implicit Feedback Matrix
^
S
I1
i2
i3
i4
u1
1
1
0
0
u2
1
0
1
0
u3
0
1
1
0
u4
0
1
0
Knowledge Graph
1
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
17. Problem formulation
u
^
u
^
I {i I | s ui 1}
Set of relevant items for u
I {i I | s ui 0}
Set of irrelevant items for u
Iu * Iu
Sample of irrelevant items for u
xui
Feature vector
D
^
xui , s ui i ( I u I u * )
TR
u
Training Set
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
18. Path-based features
path acyclic sequence of relations ( s , .. rl , .. rL )
u3 s i2 p2 e1 p1 i1
xui ( j )
(s, p2 , p1)
# pathui ( j )
D
# path
d 1
ui
(d )
Frequency of pathj in the sub-graph
related to u ad i
• The more the paths, the more the item is relevant.
• Different paths have different meaning.
• Not all types of paths are relevant.
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
20. Path-based features
path1 (s, s, s) : 1
i1
e1
u1
e3
u2
i2
e2
u3
i3
u4
i4
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
e5
e4
21. Path-based features
path1 (s, s, s) : 2
i1
e1
u1
e3
u2
i2
e2
u3
i3
u4
i4
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
e5
e4
22. Path-based features
path1 (s, s, s) : 2
path2 (s, p2, p1) : 1
i1
e1
u1
e3
u2
i2
e2
u3
i3
u4
i4
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
e5
e4
23. Path-based features
path1 (s, s, s) : 2
path2 (s, p2, p1) : 2
i1
e1
u1
e3
u2
i2
e2
u3
i3
u4
i4
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
e5
e4
24. Path-based features
path1 (s, s, s) : 2
path2 (s, p2, p1) : 2
path3 (s, p2, p3, p1) : 1
i1
e1
u1
e3
u2
i2
e2
u3
i3
u4
i4
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
e5
e4
25. Path-based features
path1 (s, s, s) : 2
path2 (s, p2, p1) : 2
path3 (s, p2, p3, p1) : 1
2
xu3i1 (1)
5
2
xu3i1 (2)
5
1
xu3i1 (3)
5
i1
e1
u1
e3
u2
i2
e2
u3
i3
u4
i4
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
e5
e4
26. Learning the ranking function
Point-wise Learning To Rank
Learn a prediction function f :
D
^
s.t. f ( xui ) sui
Assumption: if f is accurate, then the ranking induced by f should
be close to the desired ranking
• Simplest LTR technique
• Very effective in practice (Yahoo! Learning to Rank Challenge best
solution was extremely randomized trees in a standard regression setting)
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
27. BagBoo
BagBoo: a scalable hybrid bagging-the-boosting model
[D. Pavlov, A. Gorodilov, C. Brunk CIKM2010]
• Combination of Random Forest (Bagging) and Gradient Boosted
Regression Trees (Boosting)
• Combines the high accuracy of gradient boosting with the resistance
to overfitting of random forests
For b=1 to B:
Tb TR
fb learn GBRT from Tb
1 B
f fb
B b 1
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
28. Evaluation Methodology
• Top-N Item recommendation task
• Evaluation methodology similar to:
[Cremonesi, Koren and Turrin, RecSys 2010]
• Evaluation with different user profile size:
given 5
given 10
User
profile
5
User profile
Test Set
10
……
given All
User profile
Test Set
10
Test Set
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
29. Datasets
Subset of Movielens mapped to DBpedia
3,792 users
2,795 movies
104,351 entities
Subset of Last.fm mapped to DBpedia
852 users
6,256 artists
150,925 entities
Mappings
http://sisinflab.poliba.it/mappingdatasets2dbpedia.zip
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
30. Evaluation of different ranking functions
Movielens
0,6
0,5
recall@5
0,4
BagBoo
0,3
GBRT
Sum
0,2
0,1
0
given 5
given 10
given 20
given 30
given 50
given All
user profile size
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
31. Evaluation of different ranking functions
Last.fm
0,6
0,5
recall@5
0,4
BagBoo
0,3
GBRT
Sum
0,2
0,1
0
given 5
given 10
given 20
given All
user profile size
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
32. Comparative approaches
MyMediaLite
• BPRMF, Bayesian Personalized Ranking for Matrix Factorization
• BPRLin, Linear Model optimized for BPR (Hybrid alg.)
• SLIM, Sparse Linear Methods for Top-N Recommender Systems
• SMRMF, Soft Margin Ranking Matrix Factorization
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
33. Comparison with other approaches
Movielens
0,6
0,5
recall@5
0,4
SPrank
BPRMF
0,3
SLIM
BPRLin
0,2
SMRMF
0,1
0
given 5
given 10
given 20
given 30
given 50
user profile size
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
given All
34. Comparison with other approaches
Last.fm
0,6
0,5
recall@5
0,4
SPrank
BPRMF
0,3
SLIM
BPRLin
0,2
SMRMF
0,1
0
given 5
given 10
given 20
given All
user profile size
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
35. Contributions
SPrank: Semantic Path-based ranking
Combination of semantic item descriptions from the Web
of Data and implicit feedback
Mining of the semantic graph using path-based features
Learning To Rank setting
Future Work:
Deeper analysis of the path-based features
Usage of other Learning To Rank approaches
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
36. Q&A
A Little Semantics Goes a Long Way.
Hendler Hypothesis
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China