Top-K Dominating Queries on Incomplete Data with Prioritiesijtsrd
Top-K dominating query returns the k objects that are dominated in a dataset. Finding dominated elements on incomplete dataset is more complicated than in case of complete dataset. In the real- time datasets the dataset can be incomplete due to various reasons such as data loss, privacy preservation or awareness problem etc. In this paper we aims to find top-k elements from an incomplete dataset by providing priority values to each dimension in the data object. Skyline based algorithm is applied for that purpose. Since the priority value is used while determining the dominance this method return the most suitable and efficient result than other previous methods. The output will be more preferable according to the users purpose. Dr. Prabha Shreeraj Nair | Prof. Dr. G. K. Awari"Top-K Dominating Queries on Incomplete Data with Priorities" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7056.pdf http://www.ijtsrd.com/computer-science/other/7056/top-k-dominating-queries-on-incomplete--data-with-priorities/dr-prabha-shreeraj-nair
Evaluating Your Learning to Rank Model: Dos and Don’ts in Offline/Online Eval...Sease
For more details:
https://sease.io/2020/04/the-importance-of-online-testing-in-learning-to-rank-part-1.html
https://sease.io/2020/05/online-testing-for-learning-to-rank-interleaving.html
Learning to rank (LTR from now on) is the application of machine learning techniques, typically supervised, in the formulation of ranking models for information retrieval systems.
With LTR becoming more and more popular (Apache Solr supports it from Jan 2017 and Elasticsearch has an Open Source plugin released in 2018), organizations struggle with the problem of how to evaluate the quality of the models they train.
This talk explores all the major points in both Offline and Online evaluation.
Setting up correct infrastructures and processes for a fair and effective evaluation of the trained models is vital for measuring the improvements/regressions of a LTR system.
The talk is intended for:
– Product Owners, Search Managers, Business Owners
– Software Engineers, Data Scientists, and Machine Learning Enthusiast
Expect to learn :
the importance of Offline testing from a business perspective
how Offline testing can be done with Open Source libraries
how to build a realistic test set from the original data set in input avoiding common mistakes in the process
the importance of Online testing from a business perspective
A/B testing and Interleaving approaches: details and Pros/ Cons
common mistakes and how they can false the obtained results
Join us as we explore real-world scenarios and dos and don’ts from the e-commerce industry!
How to Build your Training Set for a Learning To Rank Project - HaystackSease
Presented by Alessandro Benedetti of Sease, Learning to Rank (LTR) is the application of machine learning techniques (typically supervised), in the formulation of ranking models for information retrieval systems.
With LTR becoming more and more popular, organizations struggle with the problem of how to collect and structure relevance signals necessary to train their ranking models.
This talk is a technical guide to explore and master various techniques to generate your training set(s) correctly and efficiently.
Expect to learn how to :
- model and collect the necessary feedback from the users (implicit or explicit)
- calculate for each training sample a relevance label that is meaningful and not ambiguous (Click Through Rate, Sales Rate ...)
- transform the raw data collected in an effective training set (in the numerical vector format most of the LTR training libraries expect)
Join us as we explore real-world scenarios and dos and don'ts from the e-commerce industry.
Data Science - Part II - Working with R & R studioDerek Kane
This tutorial will go through a basic primer for individuals who want to get started with predictive analytics through downloading the open source (FREE) language R. I will go through some tips to get up and started and building predictive models ASAP.
Search Quality Evaluation to Help Reproducibility : an Open Source ApproachAlessandro Benedetti
Every information retrieval practitioner ordinarily struggles with the task of evaluating how well a search engine is performing and to reproduce the performance achieved in a specific point in time.
Improving the correctness and effectiveness of a search system requires a set of tools which help measuring the direction where the system is going.
Additionally it is extremely important to track the evolution of the search system in time and to be able to reproduce and measure the same performance (through metrics of interest such as precison@k, recall, NDCG@k...).
The talk will describe the Rated Ranking Evaluator from a researcher and software engineer perspective.
RRE is an open source search quality evaluation tool, that can be used to produce a set of reports about the quality of a system, iteration after iteration and that could be integrated within a continuous integration infrastructure to monitor quality metrics after each release .
Focus of the talk will be to raise public awareness of the topic of search quality evaluation and reproducibility describing how RRE could help the industry.
Rated Ranking Evaluator: An Open Source Approach for Search Quality EvaluationAlessandro Benedetti
Every team working on Information Retrieval software struggles with the task of evaluating how well their system performs in terms of search quality(at a specific point in time and historically).
Evaluating search quality is important both to understand and size the improvement or regression of your search application across the development cycles, and to communicate such progress to relevant stakeholders.
To satisfy these requirements an helpful tool must be:
flexible and highly configurable for a technical user
immediate, visual and concise for an optimal business utilization
In the industry, and especially in the open source community, the landscape is quite fragmented: such requirements are often achieved using ad-hoc partial solutions that each time require a considerable amount of development and customization effort.
To provide a standard, unified and approachable technology, we developed the Rated Ranking Evaluator (RRE), an open source tool for evaluating and measuring the search quality of a given search infrastructure. RRE is modular, compatible with multiple search technologies and easy to extend. It is composed by a core library and a set of modules and plugins that give it the flexibility to be integrated in automated evaluation processes and in continuous integrations flows.
This talk will introduce RRE, it will describe its latest developments and demonstrate how it can be integrated in a project to measure and assess the search quality of your search application.
The focus of the presentation will be on a live demo showing an example project with a set of initial relevancy issues that we will solve iteration after iteration: using RRE output feedbacks to gradually drive the improvement process until we reach an optimal balance between quality evaluation measures.
Top-K Dominating Queries on Incomplete Data with Prioritiesijtsrd
Top-K dominating query returns the k objects that are dominated in a dataset. Finding dominated elements on incomplete dataset is more complicated than in case of complete dataset. In the real- time datasets the dataset can be incomplete due to various reasons such as data loss, privacy preservation or awareness problem etc. In this paper we aims to find top-k elements from an incomplete dataset by providing priority values to each dimension in the data object. Skyline based algorithm is applied for that purpose. Since the priority value is used while determining the dominance this method return the most suitable and efficient result than other previous methods. The output will be more preferable according to the users purpose. Dr. Prabha Shreeraj Nair | Prof. Dr. G. K. Awari"Top-K Dominating Queries on Incomplete Data with Priorities" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7056.pdf http://www.ijtsrd.com/computer-science/other/7056/top-k-dominating-queries-on-incomplete--data-with-priorities/dr-prabha-shreeraj-nair
Evaluating Your Learning to Rank Model: Dos and Don’ts in Offline/Online Eval...Sease
For more details:
https://sease.io/2020/04/the-importance-of-online-testing-in-learning-to-rank-part-1.html
https://sease.io/2020/05/online-testing-for-learning-to-rank-interleaving.html
Learning to rank (LTR from now on) is the application of machine learning techniques, typically supervised, in the formulation of ranking models for information retrieval systems.
With LTR becoming more and more popular (Apache Solr supports it from Jan 2017 and Elasticsearch has an Open Source plugin released in 2018), organizations struggle with the problem of how to evaluate the quality of the models they train.
This talk explores all the major points in both Offline and Online evaluation.
Setting up correct infrastructures and processes for a fair and effective evaluation of the trained models is vital for measuring the improvements/regressions of a LTR system.
The talk is intended for:
– Product Owners, Search Managers, Business Owners
– Software Engineers, Data Scientists, and Machine Learning Enthusiast
Expect to learn :
the importance of Offline testing from a business perspective
how Offline testing can be done with Open Source libraries
how to build a realistic test set from the original data set in input avoiding common mistakes in the process
the importance of Online testing from a business perspective
A/B testing and Interleaving approaches: details and Pros/ Cons
common mistakes and how they can false the obtained results
Join us as we explore real-world scenarios and dos and don’ts from the e-commerce industry!
How to Build your Training Set for a Learning To Rank Project - HaystackSease
Presented by Alessandro Benedetti of Sease, Learning to Rank (LTR) is the application of machine learning techniques (typically supervised), in the formulation of ranking models for information retrieval systems.
With LTR becoming more and more popular, organizations struggle with the problem of how to collect and structure relevance signals necessary to train their ranking models.
This talk is a technical guide to explore and master various techniques to generate your training set(s) correctly and efficiently.
Expect to learn how to :
- model and collect the necessary feedback from the users (implicit or explicit)
- calculate for each training sample a relevance label that is meaningful and not ambiguous (Click Through Rate, Sales Rate ...)
- transform the raw data collected in an effective training set (in the numerical vector format most of the LTR training libraries expect)
Join us as we explore real-world scenarios and dos and don'ts from the e-commerce industry.
Data Science - Part II - Working with R & R studioDerek Kane
This tutorial will go through a basic primer for individuals who want to get started with predictive analytics through downloading the open source (FREE) language R. I will go through some tips to get up and started and building predictive models ASAP.
Search Quality Evaluation to Help Reproducibility : an Open Source ApproachAlessandro Benedetti
Every information retrieval practitioner ordinarily struggles with the task of evaluating how well a search engine is performing and to reproduce the performance achieved in a specific point in time.
Improving the correctness and effectiveness of a search system requires a set of tools which help measuring the direction where the system is going.
Additionally it is extremely important to track the evolution of the search system in time and to be able to reproduce and measure the same performance (through metrics of interest such as precison@k, recall, NDCG@k...).
The talk will describe the Rated Ranking Evaluator from a researcher and software engineer perspective.
RRE is an open source search quality evaluation tool, that can be used to produce a set of reports about the quality of a system, iteration after iteration and that could be integrated within a continuous integration infrastructure to monitor quality metrics after each release .
Focus of the talk will be to raise public awareness of the topic of search quality evaluation and reproducibility describing how RRE could help the industry.
Rated Ranking Evaluator: An Open Source Approach for Search Quality EvaluationAlessandro Benedetti
Every team working on Information Retrieval software struggles with the task of evaluating how well their system performs in terms of search quality(at a specific point in time and historically).
Evaluating search quality is important both to understand and size the improvement or regression of your search application across the development cycles, and to communicate such progress to relevant stakeholders.
To satisfy these requirements an helpful tool must be:
flexible and highly configurable for a technical user
immediate, visual and concise for an optimal business utilization
In the industry, and especially in the open source community, the landscape is quite fragmented: such requirements are often achieved using ad-hoc partial solutions that each time require a considerable amount of development and customization effort.
To provide a standard, unified and approachable technology, we developed the Rated Ranking Evaluator (RRE), an open source tool for evaluating and measuring the search quality of a given search infrastructure. RRE is modular, compatible with multiple search technologies and easy to extend. It is composed by a core library and a set of modules and plugins that give it the flexibility to be integrated in automated evaluation processes and in continuous integrations flows.
This talk will introduce RRE, it will describe its latest developments and demonstrate how it can be integrated in a project to measure and assess the search quality of your search application.
The focus of the presentation will be on a live demo showing an example project with a set of initial relevancy issues that we will solve iteration after iteration: using RRE output feedbacks to gradually drive the improvement process until we reach an optimal balance between quality evaluation measures.
How to Build your Training Set for a Learning To Rank ProjectSease
Learning to rank (LTR from now on) is the application of machine learning techniques, typically supervised, in the formulation of ranking models for information retrieval systems.
With LTR becoming more and more popular (Apache Solr supports it from Jan 2017), organisations struggle with the problem of how to collect and structure relevance signals necessary to train their ranking models.
This talk is a technical guide to explore and master various techniques to generate your training set(s) correctly and efficiently.
Expect to learn how to :
– model and collect the necessary feedback from the users (implicit or explicit)
– calculate for each training sample a relevance label which is meaningful and not ambiguous (Click Through Rate, Sales Rate …)
– transform the raw data collected in an effective training set (in the numerical vector format most of the LTR training library expect)
Join us as we explore real world scenarios and dos and don’ts from the e-commerce industry.
Rated Ranking Evaluator Enterprise: the next generation of free Search Qualit...Sease
RRE is an open-source search quality evaluation tool that can be used to produce a set of reports about the quality of a system, iteration after iteration, and that can be integrated within a continuous integration infrastructure to monitor quality metrics after each release.
Many aspects remained problematic though:
– how to directly evaluate a middle layer search-API that communicates with Apache Solr or Elasticsearch?
– how to easily generate explicit and implicit ratings without spending hours on tedious json files?
– how to better explore the evaluation results? with nice widgets and interesting insights?
Rated Ranking Evaluator Enterprise solves these problems and much more.
Join us as we introduce the next generation of open-source search quality evaluation tools, exploring the internals and real-world scenarios!
Search Quality Evaluation: a Developer PerspectiveAndrea Gazzarini
Search quality evaluation is an ever-green topic every search engineer ordinarily struggles with. Improving the correctness and effectiveness of a search system requires a set of tools which help measuring the direction where the system is going.
The slides will focus on how a search quality evaluation tool can be seen under a practical developer perspective, how it could be used for producing a deliverable artifact and how it could be integrated within a continuous integration infrastructure.
Feature Selection for Document RankingAndrea Gigli
Feature selection for Machine Learning applied to Document Ranking (aka L2R, LtR, LETOR). Contains empirical results on Yahoo! and Bing public available Web Search Engine data.
Being your core domain involving real world entities ( such as hotels, restaurant, cars ...) or text documents, searching for similar entities, given one in input, is a very common use case for most of the systems that involve information retrieval. This presentation will start describing how much this problem is present across a variety of different scenarios and how you can use the More Like This feature in the Apache Lucene library to solve it. Building on the introduction the focus will be on how the More Like This module internally works, all the components involved end to end, BM25 text similarity metric and how this has been included through a cospicuos refactor and testing process. The presentation will include real world usage examples and future developments such as improved query building through positional phrase queries and term relevancy scoring pluggability.
Data Patterns - A Native Open Source Data Profiling Tool for HPCC SystemsHPCC Systems
As part of the 2018 HPCC Systems Summit Community Day event:
Data profiling is a technique used to uncover information about a source of data. Information such as the shape or accuracy of the data is extremely useful during data discovery (when you're exploring a new dataset) or when verifying that updated data appears to be a valid replacement for old data. DataPatterns, an open sourced ECL bundle for HPCC Systems, offers a native function macro for data profiling that is easy to use and supports a number of options for tuning the profile result. This talk will briefly explore the bundle's profile feature and options.
Dan Camper has been with LexisNexis Risk for four years and is a Senior Architect in the Solutions Lab Group. He has worked for Apple and Dun & Bradstreet, and he ran his own custom programming shop for a decade. He's been writing software professionally for over 35 years and has worked on a myriad of systems, using a lot of different programming languages. He thinks ECL is pretty neat.
Literature Survey: Clustering TechniqueEditor IJCATR
Clustering is a partition of data into the groups of similar or dissimilar objects. Clustering is unsupervised learning
technique helps to find out hidden patterns of Data Objects. These hidden patterns represent a data concept. Clustering is used in many
data mining applications for data analysis by finding data patterns. There is a number of clustering techniques and algorithms are
available to cluster the data object. According to the type of data object and structure appropriate clustering technique is selected. This
survey focuses on the clustering techniques for their input attribute data type, their input parameters and output. The main objective is
not to understand the actual working of clustering technique. Instead, the input data requirement and input parameters of clustering
technique are focused.
Haystack London - Search Quality Evaluation, Tools and Techniques Andrea Gazzarini
Every search engineer ordinarily struggles with the task of evaluating how well a search engine is performing. Improving the correctness and effectiveness of a search system requires a set of tools which help measuring the direction where the system is going. The talk will describe the Rated Ranking Evaluator from a developer perspective. RRE is an open source search quality evaluation tool, that could be used for producing a set of deliverable reports and that could be integrated within a continuous integration infrastructure.
A statistical and schema independent approach to determine equivalent properties between linked datasets. The approach utilizes interlinking between datasets and property extensions to understand the equivalence of properties.
Recommender Systems with Apache Spark's ALS FunctionWill Johnson
A quick visual guide to recommender systems (user based, item based, and matrix factorization) and the code behind making an apache spark MatrxFactorization Model with the ALS function.
To download please go to: http://www.intelligentmining.com/knowledge-base.html
Slides as presented by Alex Lin to the NYC Predictive Analytics Meetup group: http://www.meetup.com/NYC-Predictive-Analytics/ on April 1, 2010 (no joke!) :)
EFFECTIVENESS PREDICTION OF MEMORY BASED CLASSIFIERS FOR THE CLASSIFICATION O...cscpconf
Classification is a step by step practice for allocating a given piece of input into any of the given
category. Classification is an essential Machine Learning technique. There are many
classification problem occurs in different application areas and need to be solved. Different
types are classification algorithms like memory-based, tree-based, rule-based, etc are widely
used. This work studies the performance of different memory based classifiers for classification
of Multivariate data set from UCI machine learning repository using the open source machine
learning tool. A comparison of different memory based classifiers used and a practical
guideline for selecting the most suited algorithm for a classification is presented. Apart fromthat some empirical criteria for describing and evaluating the best classifiers are discussed
Partner Webinar: Recommendation Engines with MongoDB and HadoopMongoDB
Personalized recommendations drive business, helping people find the products they want, the news they need, and the music they didn't know they would love. Despite the obvious advantages, many companies either don't have recommendations or don't leverage their data to make good ones. Too many recommendation engines are black-box algorithms that are hard to change or don't scale well. Using the same recommendation techniques as used at StubHub, Viacom, and AP, this technical webinar will show you how to load your data from MongoDB into Hadoop, generate recommendations, and then put those recommendations into MongoDB, ready to serve end-users. This webinar will prepare you to build a custom recommender for your company that is highly scalable, easy to understand, and built on open-source technology.
K Young: About the speaker
K Young is the CEO of Mortar Data. Mortar serves data scientists and engineers with a service that makes creating and operating high-scale data pipelines easy. Mortar contributes to several open source projects including Pig, Luigi, and the Mongo-Hadoop connector. Prior to founding Mortar Data, K built software that reaches one in ten public school students in the U.S. He holds a Computer Science degree from Rice University.
How to Build your Training Set for a Learning To Rank ProjectSease
Learning to rank (LTR from now on) is the application of machine learning techniques, typically supervised, in the formulation of ranking models for information retrieval systems.
With LTR becoming more and more popular (Apache Solr supports it from Jan 2017), organisations struggle with the problem of how to collect and structure relevance signals necessary to train their ranking models.
This talk is a technical guide to explore and master various techniques to generate your training set(s) correctly and efficiently.
Expect to learn how to :
– model and collect the necessary feedback from the users (implicit or explicit)
– calculate for each training sample a relevance label which is meaningful and not ambiguous (Click Through Rate, Sales Rate …)
– transform the raw data collected in an effective training set (in the numerical vector format most of the LTR training library expect)
Join us as we explore real world scenarios and dos and don’ts from the e-commerce industry.
Rated Ranking Evaluator Enterprise: the next generation of free Search Qualit...Sease
RRE is an open-source search quality evaluation tool that can be used to produce a set of reports about the quality of a system, iteration after iteration, and that can be integrated within a continuous integration infrastructure to monitor quality metrics after each release.
Many aspects remained problematic though:
– how to directly evaluate a middle layer search-API that communicates with Apache Solr or Elasticsearch?
– how to easily generate explicit and implicit ratings without spending hours on tedious json files?
– how to better explore the evaluation results? with nice widgets and interesting insights?
Rated Ranking Evaluator Enterprise solves these problems and much more.
Join us as we introduce the next generation of open-source search quality evaluation tools, exploring the internals and real-world scenarios!
Search Quality Evaluation: a Developer PerspectiveAndrea Gazzarini
Search quality evaluation is an ever-green topic every search engineer ordinarily struggles with. Improving the correctness and effectiveness of a search system requires a set of tools which help measuring the direction where the system is going.
The slides will focus on how a search quality evaluation tool can be seen under a practical developer perspective, how it could be used for producing a deliverable artifact and how it could be integrated within a continuous integration infrastructure.
Feature Selection for Document RankingAndrea Gigli
Feature selection for Machine Learning applied to Document Ranking (aka L2R, LtR, LETOR). Contains empirical results on Yahoo! and Bing public available Web Search Engine data.
Being your core domain involving real world entities ( such as hotels, restaurant, cars ...) or text documents, searching for similar entities, given one in input, is a very common use case for most of the systems that involve information retrieval. This presentation will start describing how much this problem is present across a variety of different scenarios and how you can use the More Like This feature in the Apache Lucene library to solve it. Building on the introduction the focus will be on how the More Like This module internally works, all the components involved end to end, BM25 text similarity metric and how this has been included through a cospicuos refactor and testing process. The presentation will include real world usage examples and future developments such as improved query building through positional phrase queries and term relevancy scoring pluggability.
Data Patterns - A Native Open Source Data Profiling Tool for HPCC SystemsHPCC Systems
As part of the 2018 HPCC Systems Summit Community Day event:
Data profiling is a technique used to uncover information about a source of data. Information such as the shape or accuracy of the data is extremely useful during data discovery (when you're exploring a new dataset) or when verifying that updated data appears to be a valid replacement for old data. DataPatterns, an open sourced ECL bundle for HPCC Systems, offers a native function macro for data profiling that is easy to use and supports a number of options for tuning the profile result. This talk will briefly explore the bundle's profile feature and options.
Dan Camper has been with LexisNexis Risk for four years and is a Senior Architect in the Solutions Lab Group. He has worked for Apple and Dun & Bradstreet, and he ran his own custom programming shop for a decade. He's been writing software professionally for over 35 years and has worked on a myriad of systems, using a lot of different programming languages. He thinks ECL is pretty neat.
Literature Survey: Clustering TechniqueEditor IJCATR
Clustering is a partition of data into the groups of similar or dissimilar objects. Clustering is unsupervised learning
technique helps to find out hidden patterns of Data Objects. These hidden patterns represent a data concept. Clustering is used in many
data mining applications for data analysis by finding data patterns. There is a number of clustering techniques and algorithms are
available to cluster the data object. According to the type of data object and structure appropriate clustering technique is selected. This
survey focuses on the clustering techniques for their input attribute data type, their input parameters and output. The main objective is
not to understand the actual working of clustering technique. Instead, the input data requirement and input parameters of clustering
technique are focused.
Haystack London - Search Quality Evaluation, Tools and Techniques Andrea Gazzarini
Every search engineer ordinarily struggles with the task of evaluating how well a search engine is performing. Improving the correctness and effectiveness of a search system requires a set of tools which help measuring the direction where the system is going. The talk will describe the Rated Ranking Evaluator from a developer perspective. RRE is an open source search quality evaluation tool, that could be used for producing a set of deliverable reports and that could be integrated within a continuous integration infrastructure.
A statistical and schema independent approach to determine equivalent properties between linked datasets. The approach utilizes interlinking between datasets and property extensions to understand the equivalence of properties.
Recommender Systems with Apache Spark's ALS FunctionWill Johnson
A quick visual guide to recommender systems (user based, item based, and matrix factorization) and the code behind making an apache spark MatrxFactorization Model with the ALS function.
To download please go to: http://www.intelligentmining.com/knowledge-base.html
Slides as presented by Alex Lin to the NYC Predictive Analytics Meetup group: http://www.meetup.com/NYC-Predictive-Analytics/ on April 1, 2010 (no joke!) :)
EFFECTIVENESS PREDICTION OF MEMORY BASED CLASSIFIERS FOR THE CLASSIFICATION O...cscpconf
Classification is a step by step practice for allocating a given piece of input into any of the given
category. Classification is an essential Machine Learning technique. There are many
classification problem occurs in different application areas and need to be solved. Different
types are classification algorithms like memory-based, tree-based, rule-based, etc are widely
used. This work studies the performance of different memory based classifiers for classification
of Multivariate data set from UCI machine learning repository using the open source machine
learning tool. A comparison of different memory based classifiers used and a practical
guideline for selecting the most suited algorithm for a classification is presented. Apart fromthat some empirical criteria for describing and evaluating the best classifiers are discussed
Partner Webinar: Recommendation Engines with MongoDB and HadoopMongoDB
Personalized recommendations drive business, helping people find the products they want, the news they need, and the music they didn't know they would love. Despite the obvious advantages, many companies either don't have recommendations or don't leverage their data to make good ones. Too many recommendation engines are black-box algorithms that are hard to change or don't scale well. Using the same recommendation techniques as used at StubHub, Viacom, and AP, this technical webinar will show you how to load your data from MongoDB into Hadoop, generate recommendations, and then put those recommendations into MongoDB, ready to serve end-users. This webinar will prepare you to build a custom recommender for your company that is highly scalable, easy to understand, and built on open-source technology.
K Young: About the speaker
K Young is the CEO of Mortar Data. Mortar serves data scientists and engineers with a service that makes creating and operating high-scale data pipelines easy. Mortar contributes to several open source projects including Pig, Luigi, and the Mongo-Hadoop connector. Prior to founding Mortar Data, K built software that reaches one in ten public school students in the U.S. He holds a Computer Science degree from Rice University.
basic Function and Terminology of Recommendation Systems. Some Algorithmic Implementation with some sample Dataset for Understanding. It contains all the Layers of RS Framework well explained.
PredictionIO - Building Applications That Predict User Behavior Through Big D...predictionio
Building Applications That Predict User Behavior Through Big Data Using Open-Source Technologies
Presented by PredictionIO at Big Data TechCon (Oct 17, 2013)
Search Quality Evaluation to Help Reproducibility: An Open-source ApproachAlessandro Benedetti
Every information retrieval practitioner ordinarily struggles with the task of evaluating how well a search engine is performing and to reproduce the performance achieved in a specific point in time.
Improving the correctness and effectiveness of a search system requires a set of tools which help measuring the direction where the system is going.
Additionally it is extremely important to track the evolution of the search system in time and to be able to reproduce and measure the same performance (through metrics of interest such as precison@k, recall, NDCG@k...).
The talk will describe the Rated Ranking Evaluator from a researcher and software engineer perspective.
RRE is an open source search quality evaluation tool, that can be used to produce a set of reports about the quality of a system, iteration after iteration and that could be integrated within a continuous integration infrastructure to monitor quality metrics after each release .
Focus of the talk will be to raise public awareness of the topic of search quality evaluation and reproducibility describing how RRE could help the industry.
The ultimate goal of a recommender system is to suggest interesting and not obvious items (e.g., products to buy, people to connect with, movies to watch, etc.) to users, based on their preferences.
The advent of the Linked Open Data (LOD) initiative in the Semantic Web gave birth to a variety of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited.
Here I present several approaches to recommender systems that leverage Linked Data knowledge bases such as DBpedia. In particular, content-based and hybrid recommendation algorithms will be discussed.
For full details about the presented approaches please refer to the full papers mentioned in this presentation.
Some highlights from Recsys 2018 presented to my team at Schibsted. Note this is a "biased" summary based on personal interest and work related to my team.
(Gaurav sawant & dhaval sawlani)bia 678 final project reportGaurav Sawant
PROJECT REPORT
• Performed memory-based collaborative filtering techniques like Cosine similarities, Pearson’s r & model-based Matrix Factorization techniques like Alternating Least Squares (ALS) method
• Studied the scalability of these methods on local machines & on Hadoop clusters
PyCon Balkans 2018 // Recommender systems - collaborative filtering and dimen...Mladen Jovanovic
Recommender systems are considered as an inevitable part of any system that is offering some kind of products/services to the final user. System complexity can range anywhere from simple webstores displaying few dozen articles to big web applications with complex architecture offering millions of items to as many users.
As the systems grew bigger, rising need of efficiently handling and presenting that amount of data in a meaningful manner emerged. Early steps were focused on good categorization of available items, improving browsing capabilities and providing intelligent search. But the main aspect of user engagement remained the same. User had to take action in order to have the items presented to them. This is where the recommender systems came in, radically changing the way how the end users are experiencing the whole platform. Instead of browsing the platform in order to discover something new and relevant, the items are directly presented to the user based on previous experience, not just from their particular history of actions but from the experience of the whole user community as well.
Over the years, variety of techniques have been developed for building recommender systems, including content-based, collaborative filtering each having their own pros and cons. We’ll cover both of them offering relative insight how to treat some of the challenges involved in dealing with this kind data like matrix sparsity, dimensionality reduction etc.
Presentation by Jacob van Etten.
CCAFS workshop titled "Using Climate Scenarios and Analogues for Designing Adaptation Strategies in Agriculture," 19-23 September in Kathmandu, Nepal.
Haystack 2019 - Rated Ranking Evaluator: an Open Source Approach for Search Q...OpenSource Connections
Every team working on Information Retrieval software struggles with the task of evaluating how well their system performs in terms of search quality(at a specific point in time and historically).
Evaluating search quality is important both to understand and size the improvement or regression of your search application across the development cycles, and to communicate such progress to relevant stakeholders.
To satisfy these requirements an helpful tool must be:
- flexible and highly configurable for a technical user
- immediate, visual and concise for an optimal business utilization
In the industry, and especially in the open source community, the landscape is quite fragmented: such requirements are often achieved using ad-hoc partial solutions that each time require a considerable amount of development and customization effort.
To provide a standard, unified and approachable technology, we developed the Rated Ranking Evaluator (RRE), an open source tool for evaluating and measuring the search quality of a given search infrastructure. RRE is modular, compatible with multiple search technologies and easy to extend. It is composed by a core library and a set of modules and plugins that give it the flexibility to be integrated in automated evaluation processes and in continuous integrations flows.
This talk will introduce RRE, it will describe its latest developments and demonstrate how it can be integrated in a project to measure and assess the search quality of your search application.
The focus of the presentation will be on a live demo showing an example project with a set of initial relevancy issues that we will solve iteration after iteration: using RRE output feedbacks to gradually drive the improvement process until we reach an optimal balance between quality evaluation measures.
Rated Ranking Evaluator: an Open Source Approach for Search Quality EvaluationSease
To provide a standard, unified and approachable technology, we developed the Rated Ranking Evaluator (RRE), an open source tool for evaluating and measuring the search quality of a given search infrastructure. RRE is modular, compatible with multiple search technologies and easy to extend. It is composed by a core library and a set of modules and plugins that give it the flexibility to be integrated in automated evaluation processes and in continuous integrations flows.
This talk will introduce RRE, it will describe its latest developments and demonstrate how it can be integrated in a project to measure and assess the search quality of your search application.
Explain Yourself: Why You Get the Recommendations You DoDatabricks
Machine learning recommender systems have supercharged the online retail environment by directly targeting what the customer wants. While customers are getting better product recommendations than ever before, in the age of GDPR there is growing concern about customer privacy and transparency with ML models. Many are asking, just why am I receiving these recommendations? While the current Implicit Collaborative Filtering (CF) algorithm in spark.ml is great for generating recommendations at scale, its currently lacks any method to explain why a particular customer is getting the recommendations they are getting. In this talk, we demonstrate a way to expand collaborative filtering so that the viewing history of a customer can be directly related to their recommendations. Why were you recommended footwear? Well, 40% of this recommendation came from browsing runners and 20% came from the shorts you recently purchased. Turns out, rethinking of the linear algebra in the current spark.ml CF implementation makes this possible. We show how this is done and demonstrate its implemented as a new feature to spark.ml, expanding the API to allow everyone to explain recommendations at scale and create a more transparent ML future.
Authors: Niels Hanson Kishori Konwar
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
2. About me Started with numerical computation on main frames, followed by many years of C and C++ systems and real time programming, followed by many years of java, JEE and enterprise apps Worked for Oracle, HP, Yahoo, Motorola, many startups and mid size companies Currently Big Data consultant using Hadoop and other cloud related technologies Interested in Distributed Computation, Big Data, NOSQL DB and Data Mining. August 11th 2011 Meetup
3. Hadoop Power of functional programming and parallel processing join hands to create Hadoop Basically parallel processing framework running on cluster of commodity machines Stateless functional programming because processing of each row of data does not depend upon any other row or any state Divide and conquer parallel processing. Data gets partitioned and each partition get processed by a separate mapper or reducer task. August 11th 2011 Meetup
4. More About Hadoop Data locality, at least for the mapper. Code gets shipped to where the data partition resides Data is replicated, partitioned and resides in Hadoop Distributed File System (HDFS) Mapper output: {k -> v}. Reducer: input {k -> List(v)} Reducer output {k -> v} Many to many shuffle between mapper output and reducer input. Lot of network IO. Simple paradigm, but surprising solves an incredible array of problems. August 11th 2011 Meetup
5. Recommendation Engine Does not require an introduction. You know it if you have visited Amazon or Netflix. We love it when they get right, hate it otherwise. Very computationally intensive, ideal for Hadoop processing. In memory based recommendation engines, the entire data set is used directly e.g collaboration filtering, content based recommendation engine In model based recommendation, a model is built first by training the data and then predictions made e.g., Bayesian, Clustering August 11th 2011 Meetup
6. Content Based recommendation A memory based system, based purely on the attributes of an item only An item with p attributes is considered as a point in a p dimensional space. Uses nearest neighbor approach. Similar items are found using distance measurement in the p dimensional space. Useful for addressing the cold start problem i.e., a new item in introduced in the inventory. Computationally intensive. Not very useful for real time recommendation. August 11th 2011 Meetup
7. Model Based Recommendation Based on traditional machine learning approach In contract to memory based algorithms, creates a learning model using the ratings as training data. The model is built offline as a batch process and saved. Model needs to be rebuilt when significant change in data is detected. Once the trained model is available, making recommendation is quick. Effective for real time recommendation. August 11th 2011 Meetup
8. Collaboration Filter In collaboration filtering based recommendation engine, recommendations are made based not only the user’s rating but also rating by other users for the same item and some other items. Hence the name collaboration filtering. Requires social data i.e., user’s interest level for an item. It could be explicit e.g., product rating or implicit based on user’s interaction and behavior in a site. More appropriate name might be user intent based recommendation engine. Two approaches. In user based, similar users are found first. In item based, similar items are found first. August 11th 2011 Meetup
9. Item Based or User Based? Item based CF is generally preferred. Similarity relationship between items is relatively static and stable, because items naturally map into many genres. User based CF is less preferred, because we humans are more complex than a laptop or smart phone (although some marketing folks may disagree). As we grow and go through life experiences, our interests change. Our similarity relationship in terms of common interests with other humans is more dynamic and change over time August 11th 2011 Meetup
10. Utility Matrix Matrix of user and item. The cell contains a value indicative of the users interest level for that item e.g., rating. Matrix is sparse The purpose of recommendation engine is to predict the values for the empty cells based on available cell values Denser the matrix, better the quality of recommendation. But generally the matrix sparse. If I have rated item A and I need recommendation, enough users must have rated A as well as other items. August 11th 2011 Meetup
12. Rating Prediction Example Let’s say we are interested in predicting r35 i.e., rating of item i5 for user u3. Item based CF : r35 = (c52 x r32 + c54 x r34) / (c52 + c54) where items i2 and i4 are similar to i5 User based CF : r35 = (c31 x r15 + c32 x r25) / (c31 +c32) where users u1 and u2 are similar to u3 cij = similarity coefficient between items i and j or users i and j and rij = rating of item j by user i August 11th 2011 Meetup
13. Rating Estimation In the previous slide, we assumed rating data for item, user pair was already available, through some rating mechanism a.k.a explicit rating. However there may not be a product rating feature available in a site. Even if the rating feature is there, many users may not use it.Evenif many users rate, explicit rating by users tend to be biased. We need a way to estimate rating based on user behavior in the site and some heuristic a.k.a implicit rating August 11th 2011 Meetup
15. Similarity computation For item based CF, the first step is finding similar items. For user based CF, the first step is finding similar users We will use Pearson Correlation Coefficient. It indicates how well a set of data points lie in a straight line. In a 2 dimensional space of 2 items, rating of the 2 items by an user is a data point. There are other similarity measure algorithms e.g., euclidian distance, cosine distance August 11th 2011 Meetup
16. Pearson Correlation Coefficient c(i,j) = cov(i,j) / (stddev(i) * stddev(j)) cov(i,j) = sum ((r(u,i) - av(r(i)) * (r(u,j) - av(r(j))) / n stddev(i) = sqrt(sum((r(u,i) - av(r(i)) ** 2) / n) stddev(j) = sqrt(sum((r(u,j) - av(r(j)) ** 2) / n) The covariance can also be expressed in this alternative form, which we will be using cov(i,j) = sum(r(u,i) * r(u,j)) / n - av(r(i)) * av(r(j) c(i,j) = Pearson correlation coefficient between product i and j cov(i,j) = Covariance of rating for products i and j stddev(i) = Std deviation of rating for product i stddev(j) = Std deviation of rating for product j r(u,i) = Rating for user u for product i av(r(i)) = Average rating for product i over all users that rated sum = Sum over all users n = Num of data points August 11th 2011 Meetup
17. Map Reduce We are going to have 2 MR jobs working in tandem for items based CF. Additional preprocessing MR jobs are also necessary to process click stream data. The first MR calculates correlation for all item pairs, based on rating data. Essentially finds similar items. The second MR takes the output of the first MR and the rating data for the user in question. The output is a list of items ranked by predicted rating August 11th 2011 Meetup
18. Correlation Map Reduce It takes two kinds of input. The first kind has item id pair and two mean and std dev values for the ratings . This is generated by another pre processor MR. The second input has item rating for all users. This is generated by another preprocessor MR analyzing click stream data. Each row is for one user along with variable number of product ratings by an user August 11th 2011 Meetup
20. Correlation Mapper Output The mapper produces two kinds of output. The first kind contains {pid1,pid2,0 -> m1,s1,m2ms2}. It’s the mean and std dev for a pid pair The second kind contains {pid1,pid2,1 -> r1xr2}. It’s the product of rating for the pidpair for some user. We are appending 0 and 1 to the mapper output key, for secondary sorting which will ensure that for a given pid pair, the reducer will receive the value of the first kind of record followed by multiple values of the second kind of mapper output August 11th 2011 Meetup
22. Correlation Reducer Partitioner based on the first two tokens of key (pid1,pid2), so that the values for the same pid pair go to the same reducer Grouping comparator on the first two tokens of key (pid1,pid2), so that all the mapper out put for the same pid pair is treated as one group and passed to the reducer in one call The reducer output is pid pair and the corresponding correlation coefficient {pid1,pid2 -> c12} For a pid pair, the reducer has at it’s disposal all the data for Pearson correlation computation. August 11th 2011 Meetup
24. Prediction Map Reduce This is the second MR that takes item correlation data which is the output of the first MR and the rating data for the target user. We are running this MR to make rating prediction and ultimately recommendation for an user. The user rating data is passed to Hadoop as so called “side data”. The mapper output consists of pid of an item as the key and the rating of the related item multiplied by the correlation coefficint and the correlation coefficient as the value. {pid1 -> rating(pid3) x c13, c13} August 11th 2011 Meetup
27. Prediction Reducer The reducer gets a pid as a key and a list of tuples as value. Each tuple consists of weighted rating of a related item and the corresponding correlation coefficient. {pid1 -> [(pid3 x c31, c31), (pid5 x c51, c51),…..] The reducer sums up the weighted rating and divides the sum by sum of correlation value. This is the final predicted rating for an item. The reducer output is an item pid and the predicted rating for the item. All that remains is to sort the predicted ratings and use the top n items for making recommendation August 11th 2011 Meetup
28. Realtime Prediction We would like to make recommendation when there is a significant event e.g., item gets put on a shopping cart. But Hadoop is an offline batch processing system. How do we circumvent that? We have to do pre computation and cache the results. There are 2 MR jobs: Correlation MR to calculate item correlation and Prediction MR to prediction rating. We should re run the 2 MR jobs as necessary when significant change in user item rating is detected August 11th 2011 Meetup
29. Pre Computation As mentioned earlier item correlation is relatively stable and only needs to be re computed when there is significant change in the utility matrix Correlation MR for item similarity should be run only after significant over all change in utility matrix has been detected, since the last run. For a given user, which is basically a row in the utility matrix, if significant change is detected e.g., new rating by the user for a product is available, we should re run rating prediction MR for the user. August 11th 2011 Meetup
30. Cold Start Problem How do we make recommendation when a new item is introduced in the inventory or a new user visits the site For new item, although we have no user interest data available we can use content based recommendation. Essentially, it’s similarity computation based on the attributes of the item only. For new user (cold user?) the problem is much harder, unless detailed user profile data is available. August 11th 2011 Meetup
31. Some Temporal Issues When does an item have enough rating data to be accurately recommendable? How to define the threshold? When is there enough user rating, to be able to get good recommendations? How to define the threshold? How to deal with old ratings, as users interest shifts with passing time? When is there enough data in the utility matrix to bootstrap the recommendation system? August 11th 2011 Meetup
32. Resources My 2 part blog posts on this topic at http://pkghosh.wordpress.com “Programming Collective Intelligence” by Toby Segaram, O’Reilly “Mining of Massive Datasets” by AnandRajaraman and Jeffrey Ullman August 11th 2011 Meetup