The document describes experiments conducted to evaluate the effects of relevant contextual features on the performance of a restaurant recommender system called Surfeous. Key findings include:
- Using a reduced subset of attributes (hours, days, accepts, cuisine) performed as well or better than using all attributes, indicating feature selection can improve efficiency.
- For recall, subsets generally outperformed a context-free approach, suggesting contextual attributes enrich recommendations.
- Fusion achieved similar precision and NDCG as the context-free approach, while rules alone provided lower performance.
Effects of relevant contextual features in the performance of a restaurant re...Blanca Alicia Vargas Govea
The document describes a restaurant recommender system called Surfeous that incorporates contextual information to make recommendations. It evaluates the impact of different contextual features on recommendation performance. Key findings include:
1) Feature selection identified a minimum relevant subset of 5 attributes (cuisine, hours, days, accepts, address) that achieved similar or better precision, recall, and NDCG scores compared to using all 23 attributes.
2) Incorporating contextual rules to match user profiles improved recommendation performance over a context-free baseline.
3) The best performing subsets were D for precision, C for recall, and D and G for NDCG, demonstrating the value of selective contextual attributes.
Este documento presenta el aprendizaje de programas teleo-reactivos (PTRs) básicos y jerárquicos para controlar un robot móvil. Se describe la representación del ambiente, el aprendizaje de PTRs básicos mediante clonación para tareas como navegación, y el aprendizaje de PTRs jerárquicos que pueden incluir otros PTRs. Los experimentos muestran la capacidad del robot para realizar tareas como navegación y clasificación de ademanes controlado por los PTRs aprendidos.
Este documento propone aplicar técnicas de minería semántica para generar reglas que mejoren los sistemas de recomendación. Describe el estado actual de los sistemas de recomendación y las limitaciones de los enfoques actuales. Luego presenta una propuesta para aplicar minería semántica para extraer reglas a partir de atributos de usuario, servicio y entorno que capturen mejor las preferencias de los usuarios. Finalmente, muestra ejemplos de reglas generadas que consideran factores como la ocupación, edad, intereses cultural
Este documento describe el aprendizaje de reglas para un sistema de recomendación contextual. Presenta la metodología utilizada, incluyendo la selección de atributos, la preparación de datos y el aprendizaje automático de reglas usando ILP. La evaluación muestra que el enfoque contextual obtiene el menor desempeño, posiblemente debido a reglas demasiado generales o con sobreajuste. Se necesitan reglas que capturen los datos sin estos problemas para mejorar las recomendaciones.
Este documento presenta un resumen breve de la historia de la ingeniería de software desde la década de 1960 hasta la década de 1990. Comenzó con el uso de grandes computadoras y lenguajes como Fortran y COBOL, luego evolucionó hacia lenguajes más estructurados como Algol y Pascal, y finalmente condujo al desarrollo de la programación orientada a objetos, interfaces gráficas de usuario y software libre.
Blanca Vargas Govea reflects on her 10 year journey pursuing a PhD degree and career in research. She started as a research assistant and became a PhD student, focusing her thesis on robotics. After receiving her PhD, she moved to new cities for postdoc research, taking on challenges in data analysis, recommender systems, and the semantic web. Along the way, she discovered a passion for teaching and dealing with the ups and downs of academic work while relying on family and friends for support. Looking to the future, she hopes to continue learning, teaching, conducting research, generating new ideas, writing, and enjoying life beyond her specialized field of work.
A cognitive psychologist's approach to data miningmaggiexyz
1) The document describes a cognitive psychologist's approach to analyzing movie rating data from Netflix to improve predictions compared to the Netflix Cinematch algorithm. 2) Key aspects discussed include measuring main effects and interactions of movies, users, and their ratings, clustering users based on experience and preferences, and calculating movie and user similarities. 3) The approach uses techniques from cognitive psychology like prototypes and exemplars as well as data mining methods to generate multiple predictive models which are then combined through linear regression to produce improved predictions.
Effects of relevant contextual features in the performance of a restaurant re...Blanca Alicia Vargas Govea
The document describes a restaurant recommender system called Surfeous that incorporates contextual information to make recommendations. It evaluates the impact of different contextual features on recommendation performance. Key findings include:
1) Feature selection identified a minimum relevant subset of 5 attributes (cuisine, hours, days, accepts, address) that achieved similar or better precision, recall, and NDCG scores compared to using all 23 attributes.
2) Incorporating contextual rules to match user profiles improved recommendation performance over a context-free baseline.
3) The best performing subsets were D for precision, C for recall, and D and G for NDCG, demonstrating the value of selective contextual attributes.
Este documento presenta el aprendizaje de programas teleo-reactivos (PTRs) básicos y jerárquicos para controlar un robot móvil. Se describe la representación del ambiente, el aprendizaje de PTRs básicos mediante clonación para tareas como navegación, y el aprendizaje de PTRs jerárquicos que pueden incluir otros PTRs. Los experimentos muestran la capacidad del robot para realizar tareas como navegación y clasificación de ademanes controlado por los PTRs aprendidos.
Este documento propone aplicar técnicas de minería semántica para generar reglas que mejoren los sistemas de recomendación. Describe el estado actual de los sistemas de recomendación y las limitaciones de los enfoques actuales. Luego presenta una propuesta para aplicar minería semántica para extraer reglas a partir de atributos de usuario, servicio y entorno que capturen mejor las preferencias de los usuarios. Finalmente, muestra ejemplos de reglas generadas que consideran factores como la ocupación, edad, intereses cultural
Este documento describe el aprendizaje de reglas para un sistema de recomendación contextual. Presenta la metodología utilizada, incluyendo la selección de atributos, la preparación de datos y el aprendizaje automático de reglas usando ILP. La evaluación muestra que el enfoque contextual obtiene el menor desempeño, posiblemente debido a reglas demasiado generales o con sobreajuste. Se necesitan reglas que capturen los datos sin estos problemas para mejorar las recomendaciones.
Este documento presenta un resumen breve de la historia de la ingeniería de software desde la década de 1960 hasta la década de 1990. Comenzó con el uso de grandes computadoras y lenguajes como Fortran y COBOL, luego evolucionó hacia lenguajes más estructurados como Algol y Pascal, y finalmente condujo al desarrollo de la programación orientada a objetos, interfaces gráficas de usuario y software libre.
Blanca Vargas Govea reflects on her 10 year journey pursuing a PhD degree and career in research. She started as a research assistant and became a PhD student, focusing her thesis on robotics. After receiving her PhD, she moved to new cities for postdoc research, taking on challenges in data analysis, recommender systems, and the semantic web. Along the way, she discovered a passion for teaching and dealing with the ups and downs of academic work while relying on family and friends for support. Looking to the future, she hopes to continue learning, teaching, conducting research, generating new ideas, writing, and enjoying life beyond her specialized field of work.
A cognitive psychologist's approach to data miningmaggiexyz
1) The document describes a cognitive psychologist's approach to analyzing movie rating data from Netflix to improve predictions compared to the Netflix Cinematch algorithm. 2) Key aspects discussed include measuring main effects and interactions of movies, users, and their ratings, clustering users based on experience and preferences, and calculating movie and user similarities. 3) The approach uses techniques from cognitive psychology like prototypes and exemplars as well as data mining methods to generate multiple predictive models which are then combined through linear regression to produce improved predictions.
IRJET- Survey of Feature Selection based on Ant ColonyIRJET Journal
This document summarizes research on feature selection methods based on ant colony optimization algorithms. It first divides common feature selection approaches into filter, wrapper, and hybrid methods. It then discusses how ant colony optimization algorithms are well-suited for feature selection problems due to their ability to handle multiple objectives. The document reviews related work applying ant colony optimization to feature selection with neural networks and support vector machines. It concludes that ant colony optimization shows promise for feature selection but requires further testing on real-world datasets.
Computing Neighbourhoods with Language Models in a Collaborative Filtering Sc...Daniel Valcarce
Slides of the presentation given at IIR 2016 for the following extended abstract:
Daniel Valcarce, Javier Parapar, Alvaro Barreiro: Computing Neighbourhoods with Language Models in a Collaborative Filtering Scenario. IIR 2016, Venice, Italy.
http://dx.doi.org/10.1007/978-3-319-30671-1_45
[SAC2014]Splitting Approaches for Context-Aware Recommendation: An Empirical ...YONG ZHENG
This document describes an empirical study that compares different context-aware recommendation approaches. It evaluates three context-aware splitting approaches (item splitting, user splitting, and UI splitting) on several datasets using different recommendation algorithms and impurity criteria for splitting. The results show that UI splitting generally performs the best when used with matrix factorization as the recommendation algorithm. The document also compares the splitting approaches to other context-aware recommendation methods like differential context modeling and context-aware matrix factorization. The goal is to better understand how different context-aware techniques compare and which may be most appropriate depending on the data and application.
IRJET- Violent Social Interaction RecognitionIRJET Journal
The document presents a method for detecting violent social interactions in surveillance videos. The method uses an adaptive appearance model and a low-rank and structured sparse matrix decomposition model to highlight signs of violence. Localized spatio-temporal features are analyzed to detect changes in motion across adjacent video frames. The method was evaluated on a benchmark dataset and showed promising results in accurately detecting violent social interactions.
Strong Heredity Models in High Dimensional Datasahirbhatnagar
The document presents a model called ECLUST for identifying predictor variables associated with a phenotype that depend on an environmental factor using high-dimensional data. ECLUST uses a 3 phase approach: 1) calculating gene similarity matrices separately for different environments, 2) clustering genes to reduce dimensionality, 3) performing penalized regression on cluster representations to identify important predictors and environment-specific interactions. Simulation results show ECLUST can accurately select important variables and outperforms other methods in variable selection and predictive performance. The method is implemented in an open source R package.
Presented by Salman Asif Siddiqui (ICIMOD) at the CGIAR-CSI Annual Meeting 2009: Mapping Our Future. March 31 - April 4, 2009, ILRI Campus, Nairobi, Kenya
This document discusses a Bayesian approach to active learning for collaborative filtering. It summarizes that collaborative filtering uses preference patterns to predict user ratings, but requires many user ratings for accuracy. Active learning aims to acquire the most informative ratings from users. Previous active learning methods only consider estimated models, which can be misleading with few ratings. The proposed method takes a full Bayesian approach, averaging expected loss over the posterior model distribution to account for model uncertainty and avoid problems from estimated models. It aims to select items that maximize reduction in expected loss when considering the full model distribution, rather than just an estimated model.
The document discusses a research project that uses a smartphone app to collect subjective travel experience data from individuals. The app will provide feedback to users about their own experiences as well as those of others. The researchers aim to see if these interventions can change travel behaviors and reduce emissions. They will draw on theories from behavioral economics, psychology, and technology acceptance. An important goal is to pilot and refine the app to make it more usable and understand its impact on travel choices over multiple trials involving both strangers and friends.
[UMAP2013] Recommendation with Differential Context WeightingYONG ZHENG
Context-aware recommender systems (CARS) adapt their recommendations to users’ specific situations. In many recommender systems, particularly those based on collaborative filtering, the contextual constraints may lead to sparsity: fewer matches between the current user context and previous situations. Our earlier work proposed an approach called differential context relaxation (DCR), in which different subsets of contextual features were applied in different components of a recommendation algorithm. In this paper, we expand on our previous work on DCR, proposing a more general approach — differential context weighting (DCW), in which contextual features are weighted. We compare DCR and DCW on two real-world datasets, and DCW demonstrates improved accuracy over DCR with comparable coverage. We also show that particle swarm optimization (PSO) can be used to efficiently determine the weights for DCW.
Context-aware Recommendation: A Quick ViewYONG ZHENG
Context-aware recommendation systems take into account additional contextual information beyond just the user and item, such as time, location, and companion. There are three main approaches: contextual prefiltering splits items or users based on context; contextual modeling directly integrates context into models like matrix factorization; and CARSKit is an open source Java library for building context-aware recommender systems.
SemanticSVD++: Incorporating Semantic Taste Evolution for Predicting RatingsMatthew Rowe
This document describes SemanticSVD++, a recommendation model that incorporates semantic taste evolution for predicting ratings. It does this by:
1) Modeling users' taste profiles over time based on their ratings of items in different semantic categories.
2) Measuring how users' tastes have changed between time periods by calculating the conditional entropy and transfer entropy between taste profiles.
3) The SemanticSVD++ model predicts ratings using static biases, category biases learned from taste profiles, and a personalization component that models preferences over semantic categories.
Record matching over multiple query result - DocumentNishna Ma
This document provides an overview of record matching techniques for query results from multiple web databases. It discusses the disadvantages of existing supervised record matching methods and introduces an unsupervised duplicate detection (UDD) algorithm. UDD uses two classifiers - a weighted component similarity summing classifier and an SVM classifier - to iteratively identify duplicate records without requiring training data. The document also reviews related literature on schema matching, metaqueriers, Bayesian decision models, and similarity functions for record linkage.
Additive Smoothing for Relevance-Based Language Modelling of Recommender Syst...Daniel Valcarce
This document summarizes a presentation on additive smoothing for relevance-based language modelling of recommender systems. It discusses using pseudo-relevance feedback and relevance models for collaborative filtering recommendations. Specifically, it examines how different collection-based smoothing techniques like Dirichlet priors, Jelinek-Mercer, and absolute discounting can demote the desired IDF effect, which promotes less popular items. The document proposes using additive smoothing, which does not demote the IDF effect. Experiments on movie recommendation datasets show additive smoothing achieves better accuracy, diversity, and novelty than other smoothing methods.
The document summarizes Kenneth Emeka Odoh's presentation on recommender systems and his solution to the WSDM Challenge competition. It includes discussions of the top solutions which used techniques like light gradient boosted machines, neural networks, and ensemble modeling. It also describes Kenneth's solution using bidirectional LSTMs with techniques like batch normalization and dropout to avoid overfitting on the time series song listening data. Overall, the presentation covered many state-of-the-art recommender system techniques for sequential and time series prediction tasks.
Language Models for Collaborative Filtering Neighbourhoods [ECIR '16 Slides]Daniel Valcarce
Slides of the presentation given at ECIR 2016 for the following paper:
Daniel Valcarce, Javier Parapar, Alvaro Barreiro: Language Models for Collaborative Filtering Neighbourhoods. ECIR 2016: 614-625
http://dx.doi.org/10.1007/978-3-319-30671-1_45
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
Spatial Semantics for Better Interoperability and Analysis: Challenges and Ex...Amit Sheth
Amit Sheth, 'Spatial Semantics for Better Interoperability and Analysis: Challenges and Experiences in Building Semantically Rich Applications in Web 3.0,' Keynote Talk, 3rd Annual Spatial Ontology Community of Practice Workshop: Development, Implementation and Use of Geo-Spatial Ontologies and Semantics, USGS, Reston, VA, December 03, 2010.
This document provides an overview of deep recommender systems and some of their shortcomings. It discusses neural network architectures like NeuMF, Wide&Deep, Neural FM, DeepFM, and DSCF that have been applied to recommendation. It also covers sequential recommendation methods, optimization techniques, and challenges like short-term rewards, manually designed architectures, isolated data, and security issues like poisoning attacks.
Restaurant recommendation system is a very popular service whose so-
phistication keeps increasing everyday.In this paper we present a per-
sonalised restaurant recommendation system which has two parts to
it. The rst part recommends users' restaurants based on their restau-
rant review history. The second part recommends business owners with
places perfect to open a restaurant with a particular cuisine where the
owner would get the best trac for the restaurant. Using Zomato data,
we built a restaurant recommendation system for the individuals and
business owners. For each user in our data we nd out the cuisine
preferences and other restrictions such as services oered, ambience,
average rating, etc. and based on that we recommend the restaurants
accordingly. We propose a metric that takes the popularity as well as
the sentiment of opinions for the food items based on the user gener-
ated reviews as opposed to other systems where which only consider
the features mentioned above to recommend restaurants.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
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Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
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Slideshare: http://www.slideshare.net/PECBCERTIFICATION
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
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IRJET- Survey of Feature Selection based on Ant ColonyIRJET Journal
This document summarizes research on feature selection methods based on ant colony optimization algorithms. It first divides common feature selection approaches into filter, wrapper, and hybrid methods. It then discusses how ant colony optimization algorithms are well-suited for feature selection problems due to their ability to handle multiple objectives. The document reviews related work applying ant colony optimization to feature selection with neural networks and support vector machines. It concludes that ant colony optimization shows promise for feature selection but requires further testing on real-world datasets.
Computing Neighbourhoods with Language Models in a Collaborative Filtering Sc...Daniel Valcarce
Slides of the presentation given at IIR 2016 for the following extended abstract:
Daniel Valcarce, Javier Parapar, Alvaro Barreiro: Computing Neighbourhoods with Language Models in a Collaborative Filtering Scenario. IIR 2016, Venice, Italy.
http://dx.doi.org/10.1007/978-3-319-30671-1_45
[SAC2014]Splitting Approaches for Context-Aware Recommendation: An Empirical ...YONG ZHENG
This document describes an empirical study that compares different context-aware recommendation approaches. It evaluates three context-aware splitting approaches (item splitting, user splitting, and UI splitting) on several datasets using different recommendation algorithms and impurity criteria for splitting. The results show that UI splitting generally performs the best when used with matrix factorization as the recommendation algorithm. The document also compares the splitting approaches to other context-aware recommendation methods like differential context modeling and context-aware matrix factorization. The goal is to better understand how different context-aware techniques compare and which may be most appropriate depending on the data and application.
IRJET- Violent Social Interaction RecognitionIRJET Journal
The document presents a method for detecting violent social interactions in surveillance videos. The method uses an adaptive appearance model and a low-rank and structured sparse matrix decomposition model to highlight signs of violence. Localized spatio-temporal features are analyzed to detect changes in motion across adjacent video frames. The method was evaluated on a benchmark dataset and showed promising results in accurately detecting violent social interactions.
Strong Heredity Models in High Dimensional Datasahirbhatnagar
The document presents a model called ECLUST for identifying predictor variables associated with a phenotype that depend on an environmental factor using high-dimensional data. ECLUST uses a 3 phase approach: 1) calculating gene similarity matrices separately for different environments, 2) clustering genes to reduce dimensionality, 3) performing penalized regression on cluster representations to identify important predictors and environment-specific interactions. Simulation results show ECLUST can accurately select important variables and outperforms other methods in variable selection and predictive performance. The method is implemented in an open source R package.
Presented by Salman Asif Siddiqui (ICIMOD) at the CGIAR-CSI Annual Meeting 2009: Mapping Our Future. March 31 - April 4, 2009, ILRI Campus, Nairobi, Kenya
This document discusses a Bayesian approach to active learning for collaborative filtering. It summarizes that collaborative filtering uses preference patterns to predict user ratings, but requires many user ratings for accuracy. Active learning aims to acquire the most informative ratings from users. Previous active learning methods only consider estimated models, which can be misleading with few ratings. The proposed method takes a full Bayesian approach, averaging expected loss over the posterior model distribution to account for model uncertainty and avoid problems from estimated models. It aims to select items that maximize reduction in expected loss when considering the full model distribution, rather than just an estimated model.
The document discusses a research project that uses a smartphone app to collect subjective travel experience data from individuals. The app will provide feedback to users about their own experiences as well as those of others. The researchers aim to see if these interventions can change travel behaviors and reduce emissions. They will draw on theories from behavioral economics, psychology, and technology acceptance. An important goal is to pilot and refine the app to make it more usable and understand its impact on travel choices over multiple trials involving both strangers and friends.
[UMAP2013] Recommendation with Differential Context WeightingYONG ZHENG
Context-aware recommender systems (CARS) adapt their recommendations to users’ specific situations. In many recommender systems, particularly those based on collaborative filtering, the contextual constraints may lead to sparsity: fewer matches between the current user context and previous situations. Our earlier work proposed an approach called differential context relaxation (DCR), in which different subsets of contextual features were applied in different components of a recommendation algorithm. In this paper, we expand on our previous work on DCR, proposing a more general approach — differential context weighting (DCW), in which contextual features are weighted. We compare DCR and DCW on two real-world datasets, and DCW demonstrates improved accuracy over DCR with comparable coverage. We also show that particle swarm optimization (PSO) can be used to efficiently determine the weights for DCW.
Context-aware Recommendation: A Quick ViewYONG ZHENG
Context-aware recommendation systems take into account additional contextual information beyond just the user and item, such as time, location, and companion. There are three main approaches: contextual prefiltering splits items or users based on context; contextual modeling directly integrates context into models like matrix factorization; and CARSKit is an open source Java library for building context-aware recommender systems.
SemanticSVD++: Incorporating Semantic Taste Evolution for Predicting RatingsMatthew Rowe
This document describes SemanticSVD++, a recommendation model that incorporates semantic taste evolution for predicting ratings. It does this by:
1) Modeling users' taste profiles over time based on their ratings of items in different semantic categories.
2) Measuring how users' tastes have changed between time periods by calculating the conditional entropy and transfer entropy between taste profiles.
3) The SemanticSVD++ model predicts ratings using static biases, category biases learned from taste profiles, and a personalization component that models preferences over semantic categories.
Record matching over multiple query result - DocumentNishna Ma
This document provides an overview of record matching techniques for query results from multiple web databases. It discusses the disadvantages of existing supervised record matching methods and introduces an unsupervised duplicate detection (UDD) algorithm. UDD uses two classifiers - a weighted component similarity summing classifier and an SVM classifier - to iteratively identify duplicate records without requiring training data. The document also reviews related literature on schema matching, metaqueriers, Bayesian decision models, and similarity functions for record linkage.
Additive Smoothing for Relevance-Based Language Modelling of Recommender Syst...Daniel Valcarce
This document summarizes a presentation on additive smoothing for relevance-based language modelling of recommender systems. It discusses using pseudo-relevance feedback and relevance models for collaborative filtering recommendations. Specifically, it examines how different collection-based smoothing techniques like Dirichlet priors, Jelinek-Mercer, and absolute discounting can demote the desired IDF effect, which promotes less popular items. The document proposes using additive smoothing, which does not demote the IDF effect. Experiments on movie recommendation datasets show additive smoothing achieves better accuracy, diversity, and novelty than other smoothing methods.
The document summarizes Kenneth Emeka Odoh's presentation on recommender systems and his solution to the WSDM Challenge competition. It includes discussions of the top solutions which used techniques like light gradient boosted machines, neural networks, and ensemble modeling. It also describes Kenneth's solution using bidirectional LSTMs with techniques like batch normalization and dropout to avoid overfitting on the time series song listening data. Overall, the presentation covered many state-of-the-art recommender system techniques for sequential and time series prediction tasks.
Language Models for Collaborative Filtering Neighbourhoods [ECIR '16 Slides]Daniel Valcarce
Slides of the presentation given at ECIR 2016 for the following paper:
Daniel Valcarce, Javier Parapar, Alvaro Barreiro: Language Models for Collaborative Filtering Neighbourhoods. ECIR 2016: 614-625
http://dx.doi.org/10.1007/978-3-319-30671-1_45
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
Spatial Semantics for Better Interoperability and Analysis: Challenges and Ex...Amit Sheth
Amit Sheth, 'Spatial Semantics for Better Interoperability and Analysis: Challenges and Experiences in Building Semantically Rich Applications in Web 3.0,' Keynote Talk, 3rd Annual Spatial Ontology Community of Practice Workshop: Development, Implementation and Use of Geo-Spatial Ontologies and Semantics, USGS, Reston, VA, December 03, 2010.
This document provides an overview of deep recommender systems and some of their shortcomings. It discusses neural network architectures like NeuMF, Wide&Deep, Neural FM, DeepFM, and DSCF that have been applied to recommendation. It also covers sequential recommendation methods, optimization techniques, and challenges like short-term rewards, manually designed architectures, isolated data, and security issues like poisoning attacks.
Restaurant recommendation system is a very popular service whose so-
phistication keeps increasing everyday.In this paper we present a per-
sonalised restaurant recommendation system which has two parts to
it. The rst part recommends users' restaurants based on their restau-
rant review history. The second part recommends business owners with
places perfect to open a restaurant with a particular cuisine where the
owner would get the best trac for the restaurant. Using Zomato data,
we built a restaurant recommendation system for the individuals and
business owners. For each user in our data we nd out the cuisine
preferences and other restrictions such as services oered, ambience,
average rating, etc. and based on that we recommend the restaurants
accordingly. We propose a metric that takes the popularity as well as
the sentiment of opinions for the food items based on the user gener-
ated reviews as opposed to other systems where which only consider
the features mentioned above to recommend restaurants.
Similar to Effects of relevant contextual features in the performance of a restaurant recommender system (20)
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
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Effects of relevant contextual features in the performance of a restaurant recommender system
1. Effects of relevant contextual features
in the performance of a restaurant
recommender system
´
Blanca Vargas-Govea, Gabriel Gonzalez-Serna, Rafael Ponce-Medell´n
ı
cenidet - Computer Science Department
blanca.vargas@cenidet.edu.mx
CARS-2011, October 23, 2011
2. Outline
1 Motivation
2 Surfeous-the test bed
3 Feature selection
4 Experiments
5 Conclusions
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13. A huge amount of data can be intrusive.
A lack of information can lead the system to generate poor
recommendations.
Approach: attribute selection, semantic models.
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15. Surfeous: approaches
Social Contextual
[Tso-Sutter et al., 2008]
items
tags
Semantic web
users
items user tags
users R + R Tu
+
item user-based CF Semantic Web Rule Language
R Ti
tags item-based CF (SWRL)
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17. Rules and relations: examples
user - service profile
person(X ) ∧ hasOccupation(X , student) ∧
restaurant(R) ∧ hasCost(R, low) → select(X , R)
user - environment profile
person(X ) ∧ isJapanese(X , true) ∧
queryPlace(X , USA) ∧ restaurant(R) ∧
isVeryClose(R, true) → select(X , R)
environment - service profile
currentWeather(today, rainy) ∧ restaurant(R) ∧
space(R, closed) → select(R)
Relations
likesFood(X , Y ) X : person, Y : cuisine-type
currentWeather(X , Y ) X : query, Y : weather
space(X , Y ) X : restaurant, Y : {closed, open}
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18. Generating recommendations
1 2 3 ambiance
city cuisine
space
accepts
latitude
Surfeous gets the user Relations are created
location and searches for An ontology is created from the attributes of the
the closer restaurants in execution time restaurant profile
4 5 Results are
6 Fusion
ranked based context-free context
Person(?x) ^ hasAge(?x, ?y) ^ Ranking
1. ---------- on the number
swrlb:greaterThanOrEqual(?y, 12) ^
2. ---------- of context only-social only-rules
swrlb:lessThanOrEqual...
3. ---------- 0% 100%
rules that hold
SWRL is applied to match ... The social results are
for each
n. ---------- added
the context models user query
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19. Feature selection [Guyon & Elisseeff, 2003, Yu et al., 2004]
Generalities Procedures
Machine learning.
Predictive performance. Original
set
Subset
Generation
Subset Subset
Evaluation
Storage requirements. Goodness
of subset
No Yes Result
Stopping
Model understanding. Criterion Validation
Data visualization.
It looks for
the minimum subset of attributes such that the resulting
probability distribution of the data classes is as close as
possible to the original distribution.
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20. Algorithm LVF (Las Vegas Filter) [Liu & Setiono, 1996]
Input: maximum number of iterations (Max), dataset (D),
number of attributes (N), allowable inconsistency rate (γ)
Output: sets of M features satisfying the inconsistency crite-
rion (Solutions)
Solutions = ∅
Cbest = N
for i = 1 to Max do
S = randomSet(seed); C = numOfFeatures(S)
if C < Cbest then
if InconCheck(S,D) < γ then
Sbest = S; Cbest = C
Solutions = S
end if
else if C = Cbest and InconCheck(S,D) < γ then
append(Solutions, S)
end if
end for 20 / 28
21. Toy example
space price franchise smoking RatingA RatingB
1 i low n y 0 0
2 i low n y 1 0
3 i low n y 2 0
4 i low n y 1 1
5 i high n n 0 1
6 i high n n 1 1
7 i high n n 2 1
8 o high y n 1 1
9 o low n n 1 1
10 o low n y 2 2
subset A subset B
matching instances: 1, 2, 3, 4 matching instances: 1, 2, 3, 4
n = 4, classes = 0,1,2,1 largest = 1 (2 n = 4, classes = 0,0,0,1 largest = 0 (3
instances) instances)
Inconsistency count = 4 - 2 = 2 Inconsistency count = 4 - 3 = 1
matching instances: 5, 6, 7 matching instances: 5, 6, 7
n = 3, classes = 0,1,2 largest = 1 (1 n = 3, classes = 1,1,1 largest = 1 (3
instances) instances)
Inconsistency count = 3 - 1 = 2 Inconsistency count = 3 - 3 = 0
Inconsistency rate = (2+2)/10 = 4/10 = 0.4 Inconsistency rate = (1+0)/10 = 1/10 = 0.1
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23. Tests with Surfeous
Purposes Experimental setup
to identify relevant Leave one out.
contextual attributes.
Seven subsets: All (23), B
to show that with the (5), C-G (4).
minimum attribute subset,
the predictive performance 10 executions for each
is at least the same as with subset.
the whole attribute set, and
Baseline: context-free,
to analyze the effects of fusion (average of the
relevant contextual intervals between 0.1 and
attributes. 0.9) and context (only
rules).
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24. Results: precision/recall/NDCG
0.09 0.35
0.08
0.30
0.07
0.25
0.06
type type
Precision
0.05 0.20
Recall
context.free context.free
0.04 0.15
fusion fusion
0.03
context 0.10 context
0.02
0.05
0.01
All B C D E F G All B C D E F G
subset subset
0.55
0.50
0.45
0.40
0.35 type
0.30
NDCG
context.free
0.25
fusion
0.20
0.15 context
0.10
0.05
All B C D E F G
subset
All (23), B (cuisine, hours, days, accepts, address), C (cuisine, hours, days),
D (hours, days, accepts, address), E(cuisine, days, accepts, address), F
(cuisine, hours, accepts, address), G (cuisine, hours, days, accepts)
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25. Precision Recall NDCG
Fusion D C D
Rules F C G
Relevant attributes: hours, days, accepts, cuisine.
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26. Precision Recall NDCG
Fusion D C D
Rules F C G
Relevant attributes: hours, days, accepts, cuisine.
For recall, the majority of the subsets outperformed the
context-free performance.
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27. Precision Recall NDCG
Fusion D C D
Rules F C G
Relevant attributes: hours, days, accepts, cuisine.
For recall, the majority of the subsets outperformed the
context-free performance.
For precision and NDCG, fusion obtained similar
performance to the context-free approach.
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28. Precision Recall NDCG
Fusion D C D
Rules F C G
Relevant attributes: hours, days, accepts, cuisine.
For recall, the majority of the subsets outperformed the
context-free performance.
For precision and NDCG, fusion obtained similar
performance to the context-free approach.
Expected items appear in the top-5 list.
25 / 28
29. Precision Recall NDCG
Fusion D C D
Rules F C G
Relevant attributes: hours, days, accepts, cuisine.
For recall, the majority of the subsets outperformed the
context-free performance.
For precision and NDCG, fusion obtained similar
performance to the context-free approach.
Expected items appear in the top-5 list.
Results suggest that the restaurant opening times and its
type of payment are likely to be the most important factors
to make a choice.
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30. Precision Recall NDCG
Fusion D C D
Rules F C G
Relevant attributes: hours, days, accepts, cuisine.
For recall, the majority of the subsets outperformed the
context-free performance.
For precision and NDCG, fusion obtained similar
performance to the context-free approach.
Expected items appear in the top-5 list.
Results suggest that the restaurant opening times and its
type of payment are likely to be the most important factors
to make a choice.
Although the performance achieved by the semantic rules
is low, they provide the social approach with features that
enriches the decision process (recall). A deep analysis of
the set of rules is needed.
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31. Conclusions and future work
By using a reduced subset of attributes, the systems
performance was not degraded. Moreover, in the fusion
approach it has been improved.
Feature selection techniques can contribute to improve the
efficiency of a contextual recommender system.
Identification of relevant contextual features facilitates a
better understanding of the decision criteria of users.
As part of our future work, we are extending the approach
to the three contextual models.
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32. Effects of relevant contextual features
in the performance of a restaurant recommender system
Blanca Vargas-Govea
blanca.vargas@cenidet.edu.mx
CARS-2011, October 23, 2011
27 / 28
34. Guyon, I. & Elisseeff, A. (2003).
An introduction to variable and feature selection.
Journal of Machine Learning Research, 3, 1157–1182.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten,
I. H. (2009).
The WEKA data mining software: an update.
SIGKDD Explorations Newsletter, 11, 10–18.
Liu, H. & Setiono, R. (1996).
A probabilistic approach to feature selection - a filter solution.
In 13th International Conference on Machine Learning (pp. 319–327).
Tso-Sutter, K. H. L., Marinho, L. B., & Schmidt-Thieme, L. (2008).
Tag-aware recommender systems by fusion of collaborative filtering
algorithms.
In Proceedings of the 2008 ACM symposium on Applied computing (pp.
1995–1999). New York, USA.
Yu, L., Liu, H., & Guyon, I. (2004).
Efficient feature selection via analysis of relevance and redundancy.
Journal of Machine Learning Research, 5, 1205–1224.
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