A tutorial on query auto-completion (QAC), which refer from 10 more search conference papers in recent years. About the development of the QAC, personalized QAC, time-sensitive QAC, QAC in mobile and the future QAC.
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
This document provides a high-level overview of business intelligence (BI) and analytics. It begins by defining BI as a set of methods, processes, architectures, applications, and technologies that gather and transform raw data into meaningful information for effective decision-making. Analytics is discussed as an evolution of BI that involves more extensive data analysis and application areas. The document then covers the BI/analytics process, discussing it from both an information process and technology perspective. Finally, common BI applications and the evolution of BI/analytics are summarized.
This is part 1 of the tutorial Xavier and Deepak gave at Recsys 2016 this year. You can find the second part http://www.slideshare.net/xamat/recsys-2016-tutorial-lessons-learned-from-building-reallife-recommender-systems
Dmitry Kan, Principal AI Scientist at Silo AI and host of the Vector Podcast [1], will give an overview of the landscape of vector search databases and their role in NLP, along with the latest news and his view on the future of vector search. Further, he will share how he and his team participated in the Billion-Scale Approximate Nearest Neighbor Challenge and improved recall by 12% over a baseline FAISS.
Presented at https://www.meetup.com/open-nlp-meetup/events/282678520/
YouTube: https://www.youtube.com/watch?v=RM0uuMiqO8s&t=179s
Follow Vector Podcast to stay up to date on this topic: https://www.youtube.com/@VectorPodcast
The document provides an overview of Vertex AI, Google Cloud's managed machine learning platform. It discusses topics such as managing datasets, building and training machine learning models using both automated and custom approaches, implementing explainable AI, and deploying models. The document also includes references to the Vertex AI documentation and contact information for further information.
MLOps and Data Quality: Deploying Reliable ML Models in ProductionProvectus
Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality
The document discusses Amazon SageMaker, a fully managed machine learning platform. It introduces several new Amazon SageMaker capabilities: Amazon SageMaker Studio, which provides an integrated development environment for machine learning; Amazon SageMaker Notebooks for easier collaboration; Amazon SageMaker Processing for automated data processing and model evaluation; Amazon SageMaker Experiments for organizing and comparing training experiments; Amazon SageMaker Debugger for automated debugging of machine learning models; Amazon SageMaker Model Monitor for continuous monitoring of models in production; and Amazon SageMaker Autopilot for automated machine learning without writing code. It also discusses how Amazon SageMaker addresses challenges in deploying and managing machine learning models at scale.
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
This document provides a high-level overview of business intelligence (BI) and analytics. It begins by defining BI as a set of methods, processes, architectures, applications, and technologies that gather and transform raw data into meaningful information for effective decision-making. Analytics is discussed as an evolution of BI that involves more extensive data analysis and application areas. The document then covers the BI/analytics process, discussing it from both an information process and technology perspective. Finally, common BI applications and the evolution of BI/analytics are summarized.
This is part 1 of the tutorial Xavier and Deepak gave at Recsys 2016 this year. You can find the second part http://www.slideshare.net/xamat/recsys-2016-tutorial-lessons-learned-from-building-reallife-recommender-systems
Dmitry Kan, Principal AI Scientist at Silo AI and host of the Vector Podcast [1], will give an overview of the landscape of vector search databases and their role in NLP, along with the latest news and his view on the future of vector search. Further, he will share how he and his team participated in the Billion-Scale Approximate Nearest Neighbor Challenge and improved recall by 12% over a baseline FAISS.
Presented at https://www.meetup.com/open-nlp-meetup/events/282678520/
YouTube: https://www.youtube.com/watch?v=RM0uuMiqO8s&t=179s
Follow Vector Podcast to stay up to date on this topic: https://www.youtube.com/@VectorPodcast
The document provides an overview of Vertex AI, Google Cloud's managed machine learning platform. It discusses topics such as managing datasets, building and training machine learning models using both automated and custom approaches, implementing explainable AI, and deploying models. The document also includes references to the Vertex AI documentation and contact information for further information.
MLOps and Data Quality: Deploying Reliable ML Models in ProductionProvectus
Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality
The document discusses Amazon SageMaker, a fully managed machine learning platform. It introduces several new Amazon SageMaker capabilities: Amazon SageMaker Studio, which provides an integrated development environment for machine learning; Amazon SageMaker Notebooks for easier collaboration; Amazon SageMaker Processing for automated data processing and model evaluation; Amazon SageMaker Experiments for organizing and comparing training experiments; Amazon SageMaker Debugger for automated debugging of machine learning models; Amazon SageMaker Model Monitor for continuous monitoring of models in production; and Amazon SageMaker Autopilot for automated machine learning without writing code. It also discusses how Amazon SageMaker addresses challenges in deploying and managing machine learning models at scale.
What really are recommendations engines nowadays?
This presentation introduces the foundations of recommendation algorithms, and covers common approaches as well as some of the most advanced techniques. Although more focused on efficiency than theoretical properties, basics of matrix algebra and optimization-based machine learning are used through the presentation.
Table of Contents:
1. Collaborative Filtering
1.1 User-User
1.2 Item-Item
1.3 User-Item
* Matrix Factorization
* Stochastic Gradient Descent (SGD)
* Truncated Singular Value Decomposition (SVD)
* Alternating Least Square (ALS)
* Deep Learning
2. Content Extraction
* Item-Item Similarities
* Deep Content Extraction: NLP, CNN, LSTM
3. Hybrid Models
4. In Production
4.1 Problematics
4.2 Solutions
4.3 Tools
Amazon SageMaker Clarify (https://aws.amazon.com/sagemaker/clarify/) provides machine learning developers with greater visibility into their training data and models so they can identify and limit bias and explain predictions. SageMaker Clarify detects potential bias during data preparation, after model training, and in your deployed model by examining attributes you specify. For instance, you can check for bias related to age in your initial dataset or in your trained model and receive a detailed report that quantifies different types of possible bias. SageMaker Clarify also includes feature importance graphs that help you explain model predictions and produces reports which can be used to support internal presentations or to identify issues with your model that you can take steps to correct.
For more information on Amazon SageMaker Clarify, please refer these links: (1) https://aws.amazon.com/sagemaker/clarify (2) https://aws.amazon.com/blogs/aws/new-amazon-sagemaker-clarify-detects-bias-and-increases-the-transparency-of-machine-learning-models (3) https://github.com/aws/amazon-sagemaker-clarify (4) Discussion and demo: https://youtu.be/cQo2ew0DQw0
Acknowledgments: Amazon SageMaker Clarify core team, Amazon AWS AI team, and partners across Amazon
The document discusses introducing machine learning to kids. It suggests that machine learning should be introduced to kids because it can help them become inventors, makers, problem solvers and help shape policy. The document also discusses how machine learning can be introduced to kids, but provides no specifics on methods or approaches.
Graphs in Retail: Know Your Customers and Make Your Recommendations Engine LearnNeo4j
This document provides an overview and agenda for a presentation on using graph databases like Neo4j for retail applications. The presentation covers introducing graph databases and Neo4j, discussing retail data types, and demonstrating use cases for customer 360 views, recommendations, supply chain management, and other areas. Case studies are presented on using Neo4j for real-time recommendations at a large retailer and real-time promotions at a top US retailer. The document concludes with an invitation for questions.
This document discusses recommender systems, including:
1. It provides an overview of recommender systems, their history, and common problems like top-N recommendation and rating prediction.
2. It then discusses what makes a good recommender system, including experiment methods like offline, user surveys, and online experiments, as well as evaluation metrics like prediction accuracy, diversity, novelty, and user satisfaction.
3. Key metrics that are important to evaluate recommender systems are discussed, such as user satisfaction, prediction accuracy, coverage, diversity, novelty, serendipity, trust, robustness, and response time. The document emphasizes selecting metrics based on business goals.
Why an AI-Powered Data Catalog Tool is Critical to Business SuccessInformatica
Imagine a fast, more efficient business thriving on trusted data-driven decisions. An intelligent data catalog can help your organization discover, organize, and inventory all data assets across the org and democratize data with the right balance of governance and flexibility. Informatica's data catalog tools are powered by AI and can automate tedious data management tasks and offer immediate recommendations based on derived business intelligence. We offer data catalog workshops globally. Visit Informatica.com to attend one near you.
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
This document outlines several common use cases for graph databases including real-time recommendations, fraud detection, network operations, master data management, knowledge graphs, and identity and access management. It provides examples of how Neo4j has been used by companies like NASA, Walmart, Accenture, Telenor, Die Bayerische, and Lufthansa to power applications in domains like healthcare, ecommerce, logistics, insurance, and digital asset management.
This document discusses how machine learning and the cloud are transforming one another. It explains that machine learning involves computers learning from data without being explicitly programmed. It then outlines how major companies are using machine learning and lists some examples. It describes how the cloud is allowing more organizations to leverage machine learning by providing services that can analyze large amounts of data. Finally, it provides details on Google Cloud's machine learning services and tools.
This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
Big Data Analytics for Banking, a Point of ViewPietro Leo
This document discusses how big data and analytics can transform the banking industry. It notes that digital transformation, enabled by big data and analytics, is creating pressures on banks from new digital native customers, large amounts of new data, new channels like mobile, and new competitors. It argues that to succeed in this new environment, banks need to build a 360-degree integrated customer view using big data, and ensure analytics are part of closed-loop business processes to create value. New applications and platforms like IBM Watson Analytics aim to make analytics more accessible and valuable to more users.
LDM Slides: How Data Modeling Fits into an Overall Enterprise ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as it relates to data and its business impact across the organization.
Join this webinar for a discussion on how a data model can be combined with an overall enterprise architecture for enhanced business value and success.
PyCaret is an open-source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within minutes in your choice of environment. This talk is a practical demo on how to use PyCaret in your existing workflows and supercharge your data science team’s productivity.
This slide present Data Analytics concept. Topics are level of analytics, CRISP-DM, data science use cases e.g., customer segmentation, churn prediction, product recommendation, demand forecasting
Exploring Opportunities in the Generative AI Value Chain.pdfDung Hoang
The article "Exploring Opportunities in the Generative AI Value Chain" by McKinsey & Company's QuantumBlack provides insights into the value created by generative artificial intelligence (AI) and its potential applications.
Recommender systems: Content-based and collaborative filteringViet-Trung TRAN
This document provides an overview of recommender systems, including content-based and collaborative filtering approaches. It discusses how content-based systems make recommendations based on item profiles and calculating similarity between user and item profiles. Collaborative filtering is described as finding similar users and making predictions based on their ratings. The document also covers evaluation metrics, complexity issues, and tips for building recommender systems.
I gave a talk at North California Business Marketing Association on Customer Data Platform with examples ranging from Uber Grayballing to Zoom's customer retention email and "dogs or muffins".
This document discusses data product architectures and provides examples of different architectures for data products, including the lambda architecture, analyst architecture, recommender architecture, and partisan discourse architecture. It also discusses common design principles for data product architectures, such as using microservices with stateful backend services and database-backed APIs. Key aspects of data product architectures include handling training data and models, making predictions via APIs, updating models and annotations, and designing flexible systems that can incorporate new models and data.
This document provides an agenda and overview for a one-day Lucene boot camp tutorial. The schedule includes sessions on introducing Lucene, indexing, analysis, searching, and performance. It also covers topics like indexing in Lucene, analyzing text, querying, sorting results, and optimizing search performance. The document seeks to help attendees understand Lucene's core capabilities through real examples, code, and data. It encourages attendees to ask questions.
What really are recommendations engines nowadays?
This presentation introduces the foundations of recommendation algorithms, and covers common approaches as well as some of the most advanced techniques. Although more focused on efficiency than theoretical properties, basics of matrix algebra and optimization-based machine learning are used through the presentation.
Table of Contents:
1. Collaborative Filtering
1.1 User-User
1.2 Item-Item
1.3 User-Item
* Matrix Factorization
* Stochastic Gradient Descent (SGD)
* Truncated Singular Value Decomposition (SVD)
* Alternating Least Square (ALS)
* Deep Learning
2. Content Extraction
* Item-Item Similarities
* Deep Content Extraction: NLP, CNN, LSTM
3. Hybrid Models
4. In Production
4.1 Problematics
4.2 Solutions
4.3 Tools
Amazon SageMaker Clarify (https://aws.amazon.com/sagemaker/clarify/) provides machine learning developers with greater visibility into their training data and models so they can identify and limit bias and explain predictions. SageMaker Clarify detects potential bias during data preparation, after model training, and in your deployed model by examining attributes you specify. For instance, you can check for bias related to age in your initial dataset or in your trained model and receive a detailed report that quantifies different types of possible bias. SageMaker Clarify also includes feature importance graphs that help you explain model predictions and produces reports which can be used to support internal presentations or to identify issues with your model that you can take steps to correct.
For more information on Amazon SageMaker Clarify, please refer these links: (1) https://aws.amazon.com/sagemaker/clarify (2) https://aws.amazon.com/blogs/aws/new-amazon-sagemaker-clarify-detects-bias-and-increases-the-transparency-of-machine-learning-models (3) https://github.com/aws/amazon-sagemaker-clarify (4) Discussion and demo: https://youtu.be/cQo2ew0DQw0
Acknowledgments: Amazon SageMaker Clarify core team, Amazon AWS AI team, and partners across Amazon
The document discusses introducing machine learning to kids. It suggests that machine learning should be introduced to kids because it can help them become inventors, makers, problem solvers and help shape policy. The document also discusses how machine learning can be introduced to kids, but provides no specifics on methods or approaches.
Graphs in Retail: Know Your Customers and Make Your Recommendations Engine LearnNeo4j
This document provides an overview and agenda for a presentation on using graph databases like Neo4j for retail applications. The presentation covers introducing graph databases and Neo4j, discussing retail data types, and demonstrating use cases for customer 360 views, recommendations, supply chain management, and other areas. Case studies are presented on using Neo4j for real-time recommendations at a large retailer and real-time promotions at a top US retailer. The document concludes with an invitation for questions.
This document discusses recommender systems, including:
1. It provides an overview of recommender systems, their history, and common problems like top-N recommendation and rating prediction.
2. It then discusses what makes a good recommender system, including experiment methods like offline, user surveys, and online experiments, as well as evaluation metrics like prediction accuracy, diversity, novelty, and user satisfaction.
3. Key metrics that are important to evaluate recommender systems are discussed, such as user satisfaction, prediction accuracy, coverage, diversity, novelty, serendipity, trust, robustness, and response time. The document emphasizes selecting metrics based on business goals.
Why an AI-Powered Data Catalog Tool is Critical to Business SuccessInformatica
Imagine a fast, more efficient business thriving on trusted data-driven decisions. An intelligent data catalog can help your organization discover, organize, and inventory all data assets across the org and democratize data with the right balance of governance and flexibility. Informatica's data catalog tools are powered by AI and can automate tedious data management tasks and offer immediate recommendations based on derived business intelligence. We offer data catalog workshops globally. Visit Informatica.com to attend one near you.
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
This document outlines several common use cases for graph databases including real-time recommendations, fraud detection, network operations, master data management, knowledge graphs, and identity and access management. It provides examples of how Neo4j has been used by companies like NASA, Walmart, Accenture, Telenor, Die Bayerische, and Lufthansa to power applications in domains like healthcare, ecommerce, logistics, insurance, and digital asset management.
This document discusses how machine learning and the cloud are transforming one another. It explains that machine learning involves computers learning from data without being explicitly programmed. It then outlines how major companies are using machine learning and lists some examples. It describes how the cloud is allowing more organizations to leverage machine learning by providing services that can analyze large amounts of data. Finally, it provides details on Google Cloud's machine learning services and tools.
This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
Big Data Analytics for Banking, a Point of ViewPietro Leo
This document discusses how big data and analytics can transform the banking industry. It notes that digital transformation, enabled by big data and analytics, is creating pressures on banks from new digital native customers, large amounts of new data, new channels like mobile, and new competitors. It argues that to succeed in this new environment, banks need to build a 360-degree integrated customer view using big data, and ensure analytics are part of closed-loop business processes to create value. New applications and platforms like IBM Watson Analytics aim to make analytics more accessible and valuable to more users.
LDM Slides: How Data Modeling Fits into an Overall Enterprise ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as it relates to data and its business impact across the organization.
Join this webinar for a discussion on how a data model can be combined with an overall enterprise architecture for enhanced business value and success.
PyCaret is an open-source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within minutes in your choice of environment. This talk is a practical demo on how to use PyCaret in your existing workflows and supercharge your data science team’s productivity.
This slide present Data Analytics concept. Topics are level of analytics, CRISP-DM, data science use cases e.g., customer segmentation, churn prediction, product recommendation, demand forecasting
Exploring Opportunities in the Generative AI Value Chain.pdfDung Hoang
The article "Exploring Opportunities in the Generative AI Value Chain" by McKinsey & Company's QuantumBlack provides insights into the value created by generative artificial intelligence (AI) and its potential applications.
Recommender systems: Content-based and collaborative filteringViet-Trung TRAN
This document provides an overview of recommender systems, including content-based and collaborative filtering approaches. It discusses how content-based systems make recommendations based on item profiles and calculating similarity between user and item profiles. Collaborative filtering is described as finding similar users and making predictions based on their ratings. The document also covers evaluation metrics, complexity issues, and tips for building recommender systems.
I gave a talk at North California Business Marketing Association on Customer Data Platform with examples ranging from Uber Grayballing to Zoom's customer retention email and "dogs or muffins".
This document discusses data product architectures and provides examples of different architectures for data products, including the lambda architecture, analyst architecture, recommender architecture, and partisan discourse architecture. It also discusses common design principles for data product architectures, such as using microservices with stateful backend services and database-backed APIs. Key aspects of data product architectures include handling training data and models, making predictions via APIs, updating models and annotations, and designing flexible systems that can incorporate new models and data.
This document provides an agenda and overview for a one-day Lucene boot camp tutorial. The schedule includes sessions on introducing Lucene, indexing, analysis, searching, and performance. It also covers topics like indexing in Lucene, analyzing text, querying, sorting results, and optimizing search performance. The document seeks to help attendees understand Lucene's core capabilities through real examples, code, and data. It encourages attendees to ask questions.
Learning by example: training users through high-quality query suggestionsClaudia Hauff
A presentation given at UvA in September 2015, discussing joint work with Morgan Harvey and David Elsweiler.
Full paper: http://dl.acm.org/citation.cfm?id=2767731
Recurrent networks and beyond by Tomas MikolovBhaskar Mitra
The document summarizes Tomas Mikolov's talk on recurrent neural networks and directions for future research. The key points are:
1) Recurrent networks have seen renewed success since 2010 due to simple tricks like gradient clipping that allow them to be trained more stably. Structurally constrained recurrent networks (SCRNs) provide longer short-term memory than simple RNNs without complex architectures.
2) While RNNs have achieved strong performance on many tasks, they struggle with algorithmic patterns requiring memorization of sequences or counting. Stack augmented RNNs add structured memory to address such limitations.
3) To build truly intelligent machines, we need to focus on developing skills like communication, learning new tasks quickly from few examples
SIGIR 2016 presentation slide for paper: Xin Qian, Jimmy Lin, and Adam Roegiest. Interleaved Evaluation for Retrospective Summarization and Prospective Notification on Document Streams. Proceedings of the 39th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2016), pages 175-184, July 2016, Pisa, Italy.
Learning to Rank Personalized Search Results in Professional NetworksViet Ha-Thuc
1) The document discusses personalized search solutions for professional networks like LinkedIn, including augmenting short queries with user profile data, calculating skill reputations to find relevant jobs, and using a personalized federated search model that considers user intent and signals from different content verticals.
2) It describes challenges like skill sparsity and outliers, and approaches used to estimate skill reputation scores and infer missing skills based on collaboration.
3) The conclusions are that text matching is not enough, and personalized learning-to-rank which considers semi-structured user data, behavior, and collaborative filtering is crucial for search.
This document provides an overview of Lucene scoring and sorting algorithms. It describes how Lucene constructs a Hits object to handle scoring and caching of search results. It explains that Lucene scores documents by calling the getScore() method on a Scorer object, which depends on the type of query. For boolean queries, it typically uses a BooleanScorer2. The scoring process advances through documents matching the query terms. Sorting requires additional memory to cache fields used for sorting.
Apache Lucene: Searching the Web and Everything Else (Jazoon07)dnaber
Apache Lucene is a free and open-source search library that provides indexing and searching capabilities. It includes Lucene Java, a core Java library, Solr, a search server with web administration, and Nutch, an open-source web crawler and search engine. Lucene Java provides indexing and searching capabilities, Solr adds web-based administration and HTTP access, and Nutch crawls websites and indexes content.
A search engine uses automated software programs called spiders that crawl the web to index pages and create a searchable database. When a user searches for keywords, the search engine software returns relevant results from the index. There are three main types of search engines - directories that are compiled by humans, hybrid engines that combine human and automated results, and meta search engines that search multiple other engines at once. Each search engine indexes pages differently and has a unique algorithm to determine search results.
Internet search engines like Google and Yahoo use programs called robots or spiders to search web pages for keywords and provide ranked search results. Google's search technology is based on PageRank, which analyzes links between websites to determine importance, while Yahoo uses its own Search Technology to analyze features of web pages like text and links. Both Google and Yahoo have large databases of web pages that are updated daily and can be accessed by anyone with an internet connection to search for information on a variety of topics.
This document provides an overview of search engines. It begins with an acknowledgement and then discusses what search engines are, their importance, and different types including crawler-based, directories, hybrid, and meta search engines. Examples are provided of popular search engines like Google and Yahoo. The document concludes with tips on how to effectively use search engines by leveraging operators like plus, minus, quotes, and OR.
The document discusses different types of search engines. It describes search engines as programs that use keywords to search websites and return relevant results. It provides examples of popular search engines like Google, Yahoo, and Ask.com. It also explains different types of search engines such as crawler-based, directory-based, specialty, hybrid, and meta search engines. Finally, it discusses how to effectively use search engines through techniques like being specific, using symbols like + and -, and using Boolean searches.
Recent and Robust Query Auto-Completion - WWW 2014 Conference Presentationstewhir
These are the presentation slides used for the WWW 2014 (Web Search) full paper: "Recent and Robust Query Auto-Completion".
The PDF full paper is available from: http://www.stewh.com/wp-content/uploads/2014/02/fp539-whiting.pdf
Semantic Need: Guiding Metadata Annotations by Questions People #askHans-Joerg Happel
In its core, the Semantic Web is about the creation, collection and interlinking of metadata on which agents can perform tasks for human users. While many tools and approaches support either the creation or usage of semantic metadata, there is neither a proper notion of metadata need, nor a related theory of guidance which metadata should be created. In this paper, we propose to analyze structured queries to help identifying missing metadata. We conduct a study on Semantic MediaWiki (SMW), one of the most popular Semantic Web applications to date, analyzing structured "ask"-queries in public SMWinstances. Based on that, we describe Semantic Need, an extension for SMW which guides contributors to provide semantic annotations, and summarize feedback from an online survey among 30 experienced SMW users.
Multimedia Answer Generation for Community Question AnsweringSWAMI06
Community question answering (cQA) services have
gained popularity over the past years. It not only allows community
members to post and answer questions but also enables general
users to seek information froma comprehensive set of well-answered
questions. However, existing cQA forums usually provide
only textual answers, which are not informative enough for many
questions. In this paper, we propose a scheme that is able to enrich
textual answers in cQA with appropriate media data. Our
scheme consists of three components: answer medium selection,
query generation for multimedia search, and multimedia data selection
and presentation. This approach automatically determines
which type of media information should be added for a textual answer.
It then automatically collects data from the web to enrich the
answer. By processing a large set of QA pairs and adding them to
a pool, our approach can enable a novel multimedia question answering
(MMQA) approach as users can find multimedia answers
by matching their questions with those in the pool. Different from a
lot ofMMQAresearch efforts that attempt to directly answer questions
with image and video data, our approach is built based on
community-contributed textual answers and thus it is able to deal
with more complex questions.We have conducted extensive experiments
on a multi-source QA dataset. The results demonstrate the
effectiveness of our approach.
Exploring Session Context using Distributed Representations of Queries and Re...Bhaskar Mitra
Search logs contain examples of frequently occurring patterns of user reformulations of queries. Intuitively, the reformulation "san francisco" → "san francisco 49ers" is semantically similar to "detroit" →"detroit lions". Likewise, "london"→"things to do in london" and "new york"→"new york tourist attractions" can also be considered similar transitions in intent. The reformulation "movies" → "new movies" and "york" → "new york", however, are clearly different despite the lexical similarities in the two reformulations. In this paper, we study the distributed representation of queries learnt by deep neural network models, such as the Convolutional Latent Semantic Model, and show that they can be used to represent query reformulations as vectors. These reformulation vectors exhibit favourable properties such as mapping semantically and syntactically similar query changes closer in the embedding space. Our work is motivated by the success of continuous space language models in capturing relationships between words and their meanings using offset vectors. We demonstrate a way to extend the same intuition to represent query reformulations.
Furthermore, we show that the distributed representations of queries and reformulations are both useful for modelling session context for query prediction tasks, such as for query auto-completion (QAC) ranking. Our empirical study demonstrates that short-term (session) history context features based on these two representations improves the mean reciprocal rank (MRR) for the QAC ranking task by more than 10% over a supervised ranker baseline. Our results also show that by using features based on both these representations together we achieve a better performance, than either of them individually.
Paper: http://research.microsoft.com/apps/pubs/default.aspx?id=244728
Beyond Collaborative Filtering: Learning to Rank Research ArticlesMaya Hristakeva
This document provides an overview of Elsevier's work using machine learning techniques like collaborative filtering and learning to rank to improve article recommendations for researchers. It discusses using collaborative filtering on browsing logs to initially recommend related articles, then using learning to rank to re-rank those recommendations higher based on features like reputation, topics, citations and user engagement data. Evaluation shows the learning to rank approach improved user engagement by 9-10% over collaborative filtering alone. Future work may explore alternative approaches like graph-based methods or deep learning.
Taking the Pain out of Data Science - RecSys Machine Learning Framework Over ...Sonya Liberman
1. The document discusses Outbrain's machine learning framework for personalized recommendations using Spark. It describes challenges in collecting and processing large datasets, building predictive models, and deploying models to production through A/B testing.
2. It outlines Outbrain's distributed machine learning framework for data collection, feature engineering, model training, evaluation, and deployment. Standardized model interfaces allow easy implementation of various algorithms and ensemble modeling.
3. The framework aims to streamline the research cycle and connect research to production through automated data preparation, simple model evaluation and simulation, and fast A/B testing and model updates in production.
Best Practices for Large Scale Text Mining ProcessingOntotext
Q&A:
NOW facilitates semantic search by having annotations attached to search strings. How compolex does that get, e.g. with wildcards between annotated strings?
NOW’s searchbox is quite basic at the moment, but still supports a few scenarios.
1. Pure concept/faceted search - search for all documents containing a concept or where a set of concepts are co-occurring. Ranking is based on frequence of occurrence.
2. Concept/faceted + Full Text search - search for both concepts and particular textual term of phrase.
3. Full text search
With search, pretty much anything can be done to customise it. For the NOW showcase we’ve kept it fairly simple, as usually every client has a slightly different case and wants to tune search in a slightly different direction.
The search in NOW is faceted which means that you search with concepts (facets) and you retrieve all documents which contain mentions of the searched concept. If you search by more than one facet the engine retrieves documents which contain mentions of both concepts but there is no restriction that they occur next to each other.
Is the tagging service expandable (say with custom ontologies)? also is it a something you offer as a service? it is unclear to me from the website.
The TAG service is used for demonstration purposes only. The models behind it are trained for annotating news articles. The pipeline is customizable for every concrete scenario, different domains and entities of interest. You can access several of our pipelines as a service through the S4 platform or you can have them hosted as an on premise solution. In some cases our clients want domain adaptation or improvements in particular area, or to tag with their internal dataset - in this case we offer again an on premise deployment and also a managed service hosted on our hardware.
Hdoes your system accomodate cluster analysis using unsupervised keyword/phrase annotation for knowledge discovery?
As much as the patterns of user behaviour are also considered knowledge discovery we employ these for suggesting related reads. Apart from these we have experience tailoring custom clustering pipelines which also rely on features like keyword and named entities.
For topic extraction how many topics can we extract? from twitter corpus wgat csn we infer?
For topic extraction we have determined that we obtain best results when suggesting 3 categories. These are taken from IPTC but only the uppermost levels which are less than 20.
The twitter corpus example is from a project Ontotext participates in called Pheme. The goal of the project is to detect rumours and to check their veracity, thus help journalists in their hunt for attractive news.
Do you provide Processing Resources and JAPE rules for GATE framework and that can be used with GATE embedded?
We are contributing to the GATE framework and everything which has been wrapped up as PRs has been included the corresponding GATE distributions.
The document describes research on enhancing recommender systems through the use of user profiles and tagging systems. It discusses how user profiles can be used to provide personalized recommendations by describing a user's interests. It presents two research papers that studied how profile similarity and rating overlap between users can improve recommendation accuracy and user confidence. It also discusses how tagging systems can be leveraged by integrating user, tag, and resource dimensions. One paper proposes a personalized recommender model for folksonomies that extends the folksonomy by combining shared tags/resources and recommends tags and resources based on a user's profile and tagging history.
A Machine Learning Approach to SPARQL Query Performance PredictionRakebul Hasan
This document discusses a machine learning approach to predicting the performance of SPARQL queries over Linked Data without using statistics about the underlying RDF data. It extracts features from SPARQL queries to represent them for machine learning algorithms. The features include algebra features from the SPARQL query expressions and graph pattern features that model the query pattern. Experiments on DBpedia data show the approach can highly accurately predict execution times for common Linked Data queries by training machine learning models on previously executed queries. Future work may incorporate additional features like bandwidth and optimize queries for Linked Data applications and query processing.
Leveraging Dynamic Query Subtopics for Time-aware Search Result DiversificationNattiya Kanhabua
Search result diversification is a common technique for tackling the problem of ambiguous and multi-faceted queries by maximizing query aspects or subtopics in a result list. In some special cases, subtopics associated to such queries can be temporally ambiguous, for instance, the query US Open is more likely to be targeting the tennis open in September, and the golf tournament in June. More precisely, users' search intent can be identified by the popularity of a subtopic with respect to the time where the query is issued. In this paper, we study search result diversification for time-sensitive queries, where the temporal dynamics of query subtopics are explicitly determined and modeled into result diversification. Unlike aforementioned work that, in general, considered only static subtopics, we leverage dynamic subtopics by analyzing two data sources (i.e., query logs and a document collection). By using these data sources, it provides the insights from different perspectives of how query subtopics change over time. Moreover, we propose novel time-aware diversification methods that leverage the identified dynamic subtopics. A key idea is to re-rank search results based on the freshness and popularity of subtopics. To this end, our experimental results show that the proposed methods can significantly improve the diversity and relevance effectiveness for time-sensitive queries in comparison with state-of-the-art methods.
Utilizing Marginal Net Utility for Recommendation in E-commerceLiangjie Hong
The document discusses utilizing marginal net utility for recommendation in e-commerce. It proposes modeling user behavior based on marginal net utility to maximize the net utility for each user. It introduces a Cobb-Douglas utility function that captures diminishing returns. It then revamps existing SVD recommendation algorithms to estimate utility based on this new function. Experiments on a real e-commerce dataset show the new approach significantly outperforms baselines in recommending re-purchases and new products.
In this video from the ISC Big Data'14 Conference, Ted Willke from Intel presents: The Analytics Frontier of the Hadoop Eco-System.
"The Hadoop MapReduce framework grew out of an effort to make it easy to express and parallelize simple computations that were routinely performed at Google. It wasn’t long before libraries, like Apache Mahout, were developed to enable matrix factorization, clustering, regression, and other more complex analyses on Hadoop. Now, many of these libraries and their workloads are migrating to Apache Spark because it supports a wider class of applications than MapReduce and is more appropriate for iterative algorithms, interactive processing, and streaming applications. What’s next beyond Spark? Where is big data analytics processing headed? How will data scientists program these systems? In this talk, we will explore the current analytics frontier, the popular debates, and discuss some potentially clever additions. We will also share the emergent data science applications and collaborative university research that inform our thinking."
Learn more:
http://www.isc-events.com/bigdata14/schedule.html
and
http://www.intel.com/content/www/us/en/software/intel-graph-solutions.html
Watch the video presentation: https://www.youtube.com/watch?v=qlfx495Ekw0
Tangram: Distributed Scheduling Framework for Apache Spark at FacebookDatabricks
Tangram is a state-of-art resource allocator and distributed scheduling framework for Spark at Facebook with hierarchical queues and a resource based container abstraction. We support scheduling and resource management for a significant portion of Facebook's data warehouse and machine learning workloads that equates to running millions of jobs across several clusters with tens of thousands of machines. In this talk, we will describe Tangram's architecture, discuss Facebook's need for a custom scheduler, and explain how Tangram schedules Spark workloads at scale. We will specifically focus on several important features around improving Spark's efficiency, usability and reliability: 1. IO-rebalancer (Tetris) Support 2. User-Fairness Queueing 3. Heuristic-Based Backfill Scheduling Optimizations.
Slides from Enterprise Search & Analytics Meetup @ Cisco Systems - http://www.meetup.com/Enterprise-Search-and-Analytics-Meetup/events/220742081/
Relevancy and Search Quality Analysis - By Mark David and Avi Rappoport
The Manifold Path to Search Quality
To achieve accurate search results, we must come to an understanding of the three pillars involved.
1. Understand your data
2. Understand your customers’ intent
3. Understand your search engine
The first path passes through Data Analysis and Text Processing.
The second passes through Query Processing, Log Analysis, and Result Presentation.
Everything learned from those explorations feeds into the final path of Relevancy Ranking.
Search quality is focused on end users finding what they want -- technical relevance is sometimes irrelevant! Working with the short head (very frequent queries) has the most return on investment for improving the search experience, tuning the results, for example, to emphasize recent documents or de-emphasize archive documents, near-duplicate detection, exposing diverse results in ambiguous situations, using synonyms, and guiding search via best bets and auto-suggest. Long-tail analysis can reveal user intent by detecting patterns, discovering related terms, and identifying the most fruitful results by aggregated behavior. all this feeds back into the regression testing, which provides reliable metrics to evaluate the changes.
By merging these insights, you can improve the quality of the search overall, in a scalable and maintainable fashion.
Enhancing Information Retrieval by Personalization Techniquesveningstonk
This document outlines the research modules proposed for a PhD thesis focused on enhancing information retrieval through personalization techniques. The research will include four modules: 1) enhancing retrieval using term association graph representation, 2) integrating document and user topic models for personalization, 3) using genetic algorithms for document re-ranking, and 4) employing ant colony optimization for query reformulation. Module 1 will represent documents as a term graph and use the graph to re-rank documents based on term associations. The methodology for Module 1 includes preprocessing, frequent itemset mining to construct the term graph, and approaches for ranking documents based on semantic associations in the graph.
This tutorial gives an overview of how search engines and machine learning techniques can be tightly coupled to address the need for building scalable recommender or other prediction based systems. Typically, most of them architect retrieval and prediction in two phases. In Phase I, a search engine returns the top-k results based on constraints expressed as a query. In Phase II, the top-k results are re-ranked in another system according to an optimization function that uses a supervised trained model. However this approach presents several issues, such as the possibility of returning sub-optimal results due to the top-k limits during query, as well as the prescence of some inefficiencies in the system due to the decoupling of retrieval and ranking.
To address this issue the authors created ML-Scoring, an open source framework that tightly integrates machine learning models into Elasticsearch, a popular search engine. ML-Scoring replaces the default information retrieval ranking function with a custom supervised model that is trained through Spark, Weka, or R that is loaded as a plugin in Elasticsearch. This tutorial will not only review basic methods in information retrieval and machine learning, but it will also walk through practical examples from loading a dataset into Elasticsearch to training a model in Spark, Weka, or R, to creating the ML-Scoring plugin for Elasticsearch. No prior experience is required in any system listed (Elasticsearch, Spark, Weka, R), though some programming experience is recommended.
RecSys 2015 Tutorial – Scalable Recommender Systems: Where Machine Learning...S. Diana Hu
Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.
MUDROD - Mining and Utilizing Dataset Relevancy from Oceanographic Dataset Me...Yongyao Jiang
MUDROD - Mining and Utilizing Dataset Relevancy from Oceanographic Dataset Metadata, Usage Metrics, and User Feedback to Improve Data Discovery and Access
Building High Available and Scalable Machine Learning ApplicationsYalçın Yenigün
This document discusses building high available and scalable machine learning products. It begins with an introduction to data-driven products and machine learning concepts like supervised and unsupervised learning. It then discusses six key challenges in building machine learning products at iyzico: 1) models need testing on real data before production, 2) response times must be under 0.1 seconds, 3) data is dynamic, 4) high availability and fail fast is required, 5) continuous delivery of machine learning models, and 6) simulating aggregated features from batch data. It provides examples of techniques used at iyzico to address these challenges like Spark for predictions, schemaless databases, circuit breakers, devops for machine learning, and Redis for
Covers key concepts of clickstream analysis and Markov Chains. Followed by 3 practical applications with the R language:
- Frequent path analysis
- Future click prediction
- Transition probabilities mapping
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
Tutorial on query auto-completion
1. Образец заголовка
Tutorial on Query Auto-
Completion
Yichen Feng
feng36 AT illinois DOT edu
University of Illinois at Urbana-
Champaign
Prepared as an assignment for CS410: Text Information Systems in Spring 2016
2. Образец заголовкаQuery Auto-Completoion
• What is Query Auto-Completion (QAC)
– Giving search suggestions based on typed
prefixes by considering the search history log,
search queries popularity, temporal factors
and personal interests.
3. Образец заголовкаQAC is important
• Faster users’ input, improve efficiency
• Suggesting possible queries
• Correct users’ typing errors
• Users may not know how to describe the
information he needed
• Speed and Accuracy
• Minimize users’ cognitive and physical
effort
5. Образец заголовкаMost Popular Completion
• Traditional QAC (Most Popular Completion)
– Query are suggested from the previous query
popularity. (Mawarkar and Malemath, 2015)
– Ranked by queries’ number of frequent
occurances
– Data Structure: TRIE
– 𝑀𝐶𝑃 𝒫 = arg max
𝑞∈∁(𝒫)
𝑤 𝑞 , 𝑤 𝑞 =
𝑓(𝑞)
𝑖∈𝒬 𝑓(𝑖)
– Ranked by queries’ number of frequent occurances
– Data Structure: TRIE
– Always treated as baseline
6. Образец заголовкаQAC Challenges
• Cannot catch the popular temporal topics
• Cannot treat different people differently
• Cannot interact with users’ behaviors (e.g.
clicks)
• Bad performance on the mobile devices
• Needed to be optimized
7. Образец заголовкаSolutions
• Time-sensitive QAC
– Robust vs. Recent
• Personalized QAC
– User behaviors
– Context based QAC
• Time-sensitive Personalized QAC (Hybrid
model)
• Optimizing search results presentation
• Term by term QAC for mobile search
• QAC for rare prefixes
8. Образец заголовкаTime-Sensitive QAC
(SIGIR 12)
• Time-sensitive: query popularity changing over time
– “di-”: Dictionary for weekday, Disney for weekend
• Key idea:
– Predicting query popularity
• Forecast quality
• Success & failure analysis
• Temporal model selector
– Rely on shorter but frequent aggregation of data, model
the overall query trends by time-series.
• Method: Time-sensitive auto-completion
– 𝑇𝑆 𝒫, 𝑡 = arg max
𝑞∈∁(𝒫)
𝑤 𝑞|𝑡 , 𝑤 𝑞|𝑡 =
𝑦𝑡(𝑞)
𝑖∈𝒬 𝑦𝑡(𝑖)
– 𝑦𝑡(𝑞): estimated frequency of query q at time t
M. Shokouhi and K. Radinsky. Time-sensitive query auto-completion. In SIGIR ’12, pages 601–610, 2012.
9. Образец заголовкаTS QAC – Recent vs. Robust
(WWW 14)
• QAC need to sufficiently rank both consistently and recently
popular queries
• Motivation: Finding optimal trade-off between recency and
robustness to achieve better QAC
• Key idea:
– Optimal tradeoff could be researched
– Each query log scenario has different temporal characteristics
• Approaches:
– Based on past popularity distributions
• Maximum Likelihood Estimation, Recent Maximum Likelihood Estimation,
Last N Query Distribution
– Based on short-range predicted query popularity
• Predicted Next N Query Distribution
– Meta approach – optimize the parameters of above apporaches
• Online Parameter Learning
S. Whiting, J. McMinn, and J. Jose. Exploring real-time temporal query auto-completion. In DIR Workshop ’13, pages 12–15
10. Образец заголовкаPersonalized QAC
(SIGIR 13)
• QAC need to suggest people differently by considering their
own interestes
• Motivation: Queries likelihoods vary drastically between
different demographic groups [Weber and Castillo, 2010] and
individuals [Teevan et al., 2011]
• Key idea:
– Features based on: Users age, gender, location, short- and long-
term history
– Novel supervised framework for leaning to personalize QAC
• Method:
– Similar labelling strategy
• Evaluating by using Mean-Reciprocal-Rank (MRR)
– Learning to rank
• Lambda-MART algorithm (boosted decision trees)
• Location is more effective
M. Shokouhi. Learning to personalize query auto-completion. In SIGIR’13 2013
11. Образец заголовка
Personalized QAC – Context
Based
(IJARCET 2015)
• Query auto-completer try to accurately predicted what user is typing
• Objective: Improve search quality by predicting the user’s query
based on context
• Key idea:
– Context
• Query similarity
• User’s recent click throughs
• Current location and time
• Keywords and sessions
• Method:
– Most Popular Completion
• Works well when context is empty
– Nearest Completion
• Works well when context exists, terrible when context is empty
– Hybrid Completion
• Combine both MPC and NC
V. Mawarkar and V. Malemath. Context Based Query Auto-Completion. In IJARCET, Volume 4 Issue 6, June 2015.
12. Образец заголовкаContext Based HCA
(IJARCET 2015)
V. Mawarkar and V. Malemath. Context Based Query Auto-Completion. In IJARCET, Volume 4 Issue 6, June 2015.
13. Образец заголовкаPersonalized QAC – User Behaviors
(SIGIR14)
• Objective: Explaining the users’ interaction
data to future improving the QAC
performance
• Contributions:
– First set High-resolution QAC query log:
• Recording every keystroke- Enable further analysis on
understanding
– Horizontal skipping bias
• First introduce and unique to QAC
– Vertical position bias
– Two-dimensional Click Model
• Model users’ behavior on PC and mobile devices
Y. Li, A. Dong, H. Wang, H. Deng, Y. Chang, C. Zhai. A Two-dimensional Click Model for Query Auto-completion. In SIGIR’ 2014
14. Образец заголовкаTwo-Dimensional Click Model
(SIGIR14)
H Model
D Model
Y. Li, A. Dong, H. Wang, H. Deng, Y. Chang, C. Zhai. A Two-dimensional Click Model for Query Auto-completion. In SIGIR’ 2014
15. Образец заголовкаTime–Sensitive Personalized QAC
(CIKM14)
• Key idea:
– Hybrid model
• Time-sensitivity
• Personalization
– Optimal time window
• Achieving better predition
• Contributions:
– Novel Hybrid Model
– New query popularity prediction method
• Ranking with Mean Reciprocal Rank (MRR)
– Effectiveness analysis
• Significantly outperforms state-of-art time-sensitive
QAC
F. Cai, S. Liang, M. D. Rijke. Time-sensitive Personalized Query Auto-completion. In CIKM’ 2014
16. Образец заголовкаTSP QAC Performances
(CIKM14)
• Tradeoff between recent and periodicity
– Have critical parameter setting for accuracy
• Baselines check
– Marginally outperforms baselines
• Fact not strongly differential features
– Effective with a longer prefix
– Available evidence matters
• Better QAC ranking
– Sufficient personal queries
– Time-sensitive popularity
F. Cai, S. Liang, M. D. Rijke. Time-sensitive Personalized Query Auto-completion. In CIKM’ 2014
17. Образец заголовка
Presenting Optimized Search
Results
(WSDM16)
• Objective:
– Selectively presenting query based on a
probabilistic model to achieve optimized search
results presentation
• Key ideas:
– Time-consuming on too many query suggestions
– Measuring the users’ time-loss
– Patient users get more benefits
• Challenges:
– Uncertain factors (e. g. intent, query suggestion
click probabilities)
– Unclear of how long users spend on scanning
M. P. Kato, K. Tanaka. To Suggest, or Not to Suggest for Queries with Diverse Intents: Optimizing Search Result Presentation. In WSDM’ 2016
18. Образец заголовка
Presenting Optimized Search
Results
(WSDM16)
• Contributions:
– Searcher model
• Interacting with query suggestions
• According to users’ multiple intents
– Optimizing Search Results Presentation (OSRP)
• Mainly focusing on ambiguous or underspecified query
– Examined effects of query suggestion on search
behaviors
• Conducting user survey
– Effectiveness of OSRP
• Patient users
• Queries with limited number of intents
M. P. Kato, K. Tanaka. To Suggest, or Not to Suggest for Queries with Diverse Intents: Optimizing Search Result Presentation. In WSDM’ 2016
19. Образец заголовкаUsers Survey
(WSDM16)
M. P. Kato, K. Tanaka. To Suggest, or Not to Suggest for Queries with Diverse Intents: Optimizing Search Result Presentation. In WSDM’ 2016
SERP (M. P. Kato and K. Tanaka)
20. Образец заголовка
Term-by-Term QAC for Mobile
Search
(WSDM16)
• Objective:
– Specialized QAC for mobile search
• Mobile Input:
– Small screen Term-by-Term QAC
– Slower input High quality QAC
– Clumsier QAC matters more than PC
• Key idea:
– Faster exploration of suggestions
– Fits for the text editing in mobile devices
S. Vargas, R. Blanco, P. Mika. Term-by-Term Query Auto-Completion for Mobile Search. In WSDM 2016
21. Образец заголовкаQuery-Term Graph
(WSDM16)
– Based on previous submitted queries
– Efficient way of
• Storing
• Retrieving
S. Vargas, R. Blanco, P. Mika. Term-by-Term Query Auto-Completion for Mobile Search. In WSDM 2016
22. Образец заголовкаQAC for Rare Prefixes
(CIKM15)
• Motivation: QAC fail when the prefix is
sufficiently rare
• Key ideas:
– Supervised model ranking synthetic
suggestions
– Query generated by mining query suffixes
– Exploring new ranking signals
• Query n-gram statistics
• Deep convolutional latent semantic model (CLSM)
S. Vargas, R. Blanco, P. Mika. Term-by-Term Query Auto-Completion for Mobile Search. In WSDM 2016
23. Образец заголовкаModel and Features
(CIKM15)
• LambdaMART model:
– Ranking using features
• N-gram based features
– Model the likelihood that candidate
suggestion is generated by the same LM as
the queries in the search logs
• CLSM based features
– Based on clickthrough data
– Effective for modelling query-document
relevance
– Training on a prefix-suffix pairs datasetB. Mitra, N. Craswell. Query Auto-Completion for Rare Prefixes. In CIKM 2015
24. Образец заголовкаQAC for Rare Prefixes
(CIKM15)
• Motivation: QAC fail when the prefix is
sufficiently rare
• Key ideas:
– Supervised model ranking synthetic
suggestions
– Query generated by mining query suffixes
– Exploring new ranking signals
• Query n-gram statistics
• Deep convolutional latent semantic model (CLSM)
B. Mitra, N. Craswell. Query Auto-Completion for Rare Prefixes. In CIKM 2015
25. Образец заголовкаFuture works
• Short range query popularity prediction
• Complex relationships between users’
behavior at different keystrokes
• More complex click models
• Model personalized temporal patterns for
active users (e.g. Professional searchers)
• Online user behavior study on mobile
• Other LM on rare prefixes
27. Образец заголовкаReferences
1. M. Shokouhi and K. Radinsky. Time-sensitive query auto-completion. In SIGIR ’12, pages
601–610, 2012.
2. S. Whiting, J. McMinn, and J. Jose. Exploring real-time temporal query auto-completion. In
DIR Workshop ’13, pages 12–15
3. M. Shokouhi. Learning to personalize query auto-completion. In SIGIR’13 2013
4. V. Mawarkar and V. Malemath. Context Based Query Auto-Completion. In IJARCET, Volume
4 Issue 6, June 2015.
5. Y. Li, A. Dong, H. Wang, H. Deng, Y. Chang, C. Zhai. A Two-dimensional Click Model for
Query Auto-completion. In SIGIR’ 2014
6. F. Cai, S. Liang, M. D. Rijke. Time-sensitive Personalized Query Auto-completion. In CIKM’
2014
7. M. P. Kato, K. Tanaka. To Suggest, or Not to Suggest for Queries with Diverse Intents:
Optimizing Search Result Presentation. In WSDM’ 2016
8. S. Vargas, R. Blanco, P. Mika. Term-by-Term Query Auto-Completion for Mobile Search. In
WSDM 2016
9. B. Mitra, N. Craswell. Query Auto-Completion for Rare Prefixes. In CIKM 2015
10. L. Li, H. Deng, A. Dong, Y. Chang, H. Zha, R. Baeza-Yates. Analyzing User’s Sequential
Behavior in Query Auto-Completion via Markov Processes. In Proc. SIGIR’15 2015.
11. M. Shokouhi. Detecting seasonal queries by time-series analysis. In Proc. SIGIR, pages
1171–1172, Beijing, China, 2011
12. R. W. White and G. Marchionini. Examining the effectiveness of real-time query expansion.
Inf. Process. Manage., 43:685–704, May 2007
13. Z. Bar-Yossef and N. Kraus. Context-sensitive query auto-completion. In WWW ’11, pages
107–116, 2011.