Daniel Tunkelang discusses three key areas - relevance, recommendations, and reputation - that LinkedIn is focusing on to improve the user experience. Relevance refers to improving search and discovery of profiles by combining different types of signals. Recommendations match members to jobs, groups, and other connections. Reputation involves identifying member expertise in skills and who the experts are in particular skills. Open problems remain around exploratory search, balancing exploration and exploitation of the data, and incentivizing the development of online reputation.
Tanvi Motwani, Lead Data Scientist, Guided Search at A9.com at MLconf ATL 2016MLconf
E-commerce Query Tagging System Using Unsupervised Training Methods: Amazon is one of the world’s largest e-commerce sites and Amazon Search powers the majority of Amazon’s sales. A key component of Amazon Search is the query understanding pipeline, which extracts appropriate semantic information used to precisely display products for billions of queries everyday. In this talk, we will go through the primary building blocks of query understanding pipeline.
Amazon Search enables users to search against structured products, hence it is necessary to extract information from queries in a format that is consistent with the structured information about the products. Query tagging is the task of semantically annotating query terms to pre-defined labels (such as brand, product-type and color). We propose a scalable system to train large-scale machine learning algorithms to solve this problem. Our system improved the precision over baseline, which is a dictionary lookup based tagger, by 10% and approximately doubled the recall.
Establish Your Personal Brand with LinkedInJo Saunders
Graduating and about to launch your career or business, then invest in building a professional LinkedIn presence to establish your personal brand and stand out from the crowd.
Enterprise Search Strategy 101 at SEF2014 in StockholmJoel Oleson
In this session on getting at your Enterprise Search Strategy we dig into the 4 pillars of Search including Context, Content, Metadata and UX. We use examples from Amazon, Google, Bing and a variety of real world SharePoint 2013 publicly accessible environments optimized for Search.
Tanvi Motwani, Lead Data Scientist, Guided Search at A9.com at MLconf ATL 2016MLconf
E-commerce Query Tagging System Using Unsupervised Training Methods: Amazon is one of the world’s largest e-commerce sites and Amazon Search powers the majority of Amazon’s sales. A key component of Amazon Search is the query understanding pipeline, which extracts appropriate semantic information used to precisely display products for billions of queries everyday. In this talk, we will go through the primary building blocks of query understanding pipeline.
Amazon Search enables users to search against structured products, hence it is necessary to extract information from queries in a format that is consistent with the structured information about the products. Query tagging is the task of semantically annotating query terms to pre-defined labels (such as brand, product-type and color). We propose a scalable system to train large-scale machine learning algorithms to solve this problem. Our system improved the precision over baseline, which is a dictionary lookup based tagger, by 10% and approximately doubled the recall.
Establish Your Personal Brand with LinkedInJo Saunders
Graduating and about to launch your career or business, then invest in building a professional LinkedIn presence to establish your personal brand and stand out from the crowd.
Enterprise Search Strategy 101 at SEF2014 in StockholmJoel Oleson
In this session on getting at your Enterprise Search Strategy we dig into the 4 pillars of Search including Context, Content, Metadata and UX. We use examples from Amazon, Google, Bing and a variety of real world SharePoint 2013 publicly accessible environments optimized for Search.
This keynote presentation describes the critical role that search and Lucene has in building next generation products that understand reputation and relevance. We also describe how data science and machine learning have been applied at LinkedIn to collect, interpret, and index data around topical reputation.
Lucene Revolution is the biggest open source conference dedicated to Apache Lucene/Solr.
Search Strategy for Enterprise SharePoint 2013 - Vancouver SharePoint SummitJoel Oleson
The Four Pillars of Search really help you focus your search planning. In this session we dig into the context, content, metadata and UX or user experience that really matter. We also dig into a variety of publicly accessible SharePoint 2013 real world search pages to demonstrate the value.
Find the 'Unfindable' with TalentBin by Monster!monsterindia
In a skills-constrained environment, a company’s ability to find, attract, and access the right professional DNA is critical to success. TalentBin by Monster not only automates and streamlines the talent discovery process via automated site monitoring, interpretation and matching, but also simplifies and automates the recruiting workflow, allowing recruiters do more in less time.
An analysis of competing social computing platforms against SharePoint 2010. A lot of context is lost without the narrative, but for those who have seen Mike Watson and/or myself present, this will be a reminder.
capabilities across four innovation areas:
* Knowledge discovery – to connect you with relevant knowledge from across the organization in real time, in the context of your work
* Content services – which unlocks knowledge in content to power business processes and compliance
* Expertise and answers – to connect you with experts, so you can resource projects, share best practices, and get questions answered; and
* Workplace insights – which help individuals, teams, and organizations ensure well-being and effectiveness
Data By The People, For The People
Daniel Tunkelang
Director, Data Science at LinkedIn
Invited Talk at the 21st ACM International Conference on Information and Knowledge Management (CIKM 2012)
LinkedIn has a unique data collection: the 175M+ members who use LinkedIn are also the content those same members access using our information retrieval products. LinkedIn members performed over 4 billion professionally-oriented searches in 2011, most of those to find and discover other people. Every LinkedIn search and recommendation is deeply personalized, reflecting the user's current employment, career history, and professional network. In this talk, I will describe some of the challenges and opportunities that arise from working with this unique corpus. I will discuss work we are doing in the areas of relevance, recommendation, and reputation, as well as the ecosystem we have developed to incent people to provide the high-quality semi-structured profiles that make LinkedIn so useful.
Bio:
Daniel Tunkelang leads the data science team at LinkedIn, which analyzes terabytes of data to produce products and insights that serve LinkedIn's members. Prior to LinkedIn, Daniel led a local search quality team at Google. Daniel was a founding employee of faceted search pioneer Endeca (recently acquired by Oracle), where he spent ten years as Chief Scientist. He has authored fourteen patents, written a textbook on faceted search, created the annual workshop on human-computer interaction and information retrieval (HCIR), and participated in the premier research conferences on information retrieval, knowledge management, databases, and data mining (SIGIR, CIKM, SIGMOD, SIAM Data Mining). Daniel holds a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.
Title:
Semantic Equivalence of e-Commerce Queries
Authors:
Aritra Mandal, Daniel Tunkelang, Zhe Wu
Presented at KDD 2023 Workshop on E-Commerce and Natural Language Processing (ECNLP 2023).
More Related Content
Similar to Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn
This keynote presentation describes the critical role that search and Lucene has in building next generation products that understand reputation and relevance. We also describe how data science and machine learning have been applied at LinkedIn to collect, interpret, and index data around topical reputation.
Lucene Revolution is the biggest open source conference dedicated to Apache Lucene/Solr.
Search Strategy for Enterprise SharePoint 2013 - Vancouver SharePoint SummitJoel Oleson
The Four Pillars of Search really help you focus your search planning. In this session we dig into the context, content, metadata and UX or user experience that really matter. We also dig into a variety of publicly accessible SharePoint 2013 real world search pages to demonstrate the value.
Find the 'Unfindable' with TalentBin by Monster!monsterindia
In a skills-constrained environment, a company’s ability to find, attract, and access the right professional DNA is critical to success. TalentBin by Monster not only automates and streamlines the talent discovery process via automated site monitoring, interpretation and matching, but also simplifies and automates the recruiting workflow, allowing recruiters do more in less time.
An analysis of competing social computing platforms against SharePoint 2010. A lot of context is lost without the narrative, but for those who have seen Mike Watson and/or myself present, this will be a reminder.
capabilities across four innovation areas:
* Knowledge discovery – to connect you with relevant knowledge from across the organization in real time, in the context of your work
* Content services – which unlocks knowledge in content to power business processes and compliance
* Expertise and answers – to connect you with experts, so you can resource projects, share best practices, and get questions answered; and
* Workplace insights – which help individuals, teams, and organizations ensure well-being and effectiveness
Data By The People, For The People
Daniel Tunkelang
Director, Data Science at LinkedIn
Invited Talk at the 21st ACM International Conference on Information and Knowledge Management (CIKM 2012)
LinkedIn has a unique data collection: the 175M+ members who use LinkedIn are also the content those same members access using our information retrieval products. LinkedIn members performed over 4 billion professionally-oriented searches in 2011, most of those to find and discover other people. Every LinkedIn search and recommendation is deeply personalized, reflecting the user's current employment, career history, and professional network. In this talk, I will describe some of the challenges and opportunities that arise from working with this unique corpus. I will discuss work we are doing in the areas of relevance, recommendation, and reputation, as well as the ecosystem we have developed to incent people to provide the high-quality semi-structured profiles that make LinkedIn so useful.
Bio:
Daniel Tunkelang leads the data science team at LinkedIn, which analyzes terabytes of data to produce products and insights that serve LinkedIn's members. Prior to LinkedIn, Daniel led a local search quality team at Google. Daniel was a founding employee of faceted search pioneer Endeca (recently acquired by Oracle), where he spent ten years as Chief Scientist. He has authored fourteen patents, written a textbook on faceted search, created the annual workshop on human-computer interaction and information retrieval (HCIR), and participated in the premier research conferences on information retrieval, knowledge management, databases, and data mining (SIGIR, CIKM, SIGMOD, SIAM Data Mining). Daniel holds a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.
Title:
Semantic Equivalence of e-Commerce Queries
Authors:
Aritra Mandal, Daniel Tunkelang, Zhe Wu
Presented at KDD 2023 Workshop on E-Commerce and Natural Language Processing (ECNLP 2023).
Helping Searchers Satisfice through Query UnderstandingDaniel Tunkelang
Behavioral economics transformed how we think about human decision making, rejecting expected utility maximization for the real world of heuristics, biases, and satisficing. In this talk, I'll argue that our thinking about search engines needs a similar transformation. I will compare the Probability Ranking Principle to expected utility maximization and offer ways that AI can help searchers satisfice through query understanding.
This was an invited talk given at the 2023 Walmart AI Summit.
Speaker Bio
Daniel Tunkelang is an independent consultant specializing in search, machine learning / AI, and data science. He completed undergraduate and master's degrees in Computer Science and Math at MIT and a PhD in computer science at CMU. He was a founding employee and chief scientist of Endeca, a search pioneer that Oracle acquired in 2011. He then led engineering and data science teams at Google and LinkedIn. He has written a book on Faceted Search, and he blogs on Medium about search-related topics — particularly query understanding. He has worked with numerous tech companies, retailers, and others, including Algolia, Apple, Canva, Coupang, eBay, Etsy, Flipkart, Home Depot, Oracle, Pinterest, Salesforce, Target, Yelp, and Zoom.
MMM, Search!
An opinionated discussion of search metrics, models, and methods. Presented to the Wikimedia Foundation on April 27, 2020.
About the Speaker
Daniel Tunkelang is an independent consultant specializing in search, discovery, machine learning / AI, and data science.
He was a founding employee of Endeca, a search pioneer that Oracle acquired. After 10 years at Endeca, he moved to Google, where he led a local search team. He then served as a director of data science and search at LinkedIn.
After leaving LinkedIn in 2015, he became an independent consultant. His clients have included Apple, eBay, Coupang, Etsy, Flipkart, Gartner, Pinterest, Salesforce, and Yelp; as well as some of the largest traditional retailers.
Daniel completed undergraduate and master's degrees in Computer Science and Math at MIT and a Ph.D. in computer science at CMU. He wrote a book on Faceted Search, published by Morgan & Claypool, and he blogs on Medium about search-related topics -- particularly about query understanding. He is also active on Twitter, LinkedIn, and Quora.
Enterprise Intelligence: Putting the Pieces Together
http://enterpriserelevance.com/kdd2016/keynote.html
These slides are for a keynote presentation delivered at the Workshop on Enterprise Intelligence, held in conjunction with the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016).
About the author:
Daniel Tunkelang is a data science and engineering executive who has built and led some of the strongest teams in the software industry. He studied computer science and math at MIT and has a PhD in computer science from CMU. He was a founding employee and chief scientist of Endeca, a search pioneer that Oracle acquired for $1.1B. He led a local search team at Google. He was a director of data science and engineering at LinkedIn, and he established their query understanding team. Daniel is a widely recognized writer and speaker. He is frequently invited to speak at academic and industry conferences, particularly in the areas of information retrieval, web science, and data science. He has written the definitive textbook on faceted search (now a standard for ecommerce sites), established an annual symposium on human-computer interaction and information retrieval, and authored 24 US patents. His social media posts have attracted over a million page views. Daniel advises and consults for companies that can benefit strategically from his expertise. His clients range from early-stage startups to "unicorn" technology companies like Etsy and Pinterest. He helps companies make decisions around algorithms, technology, product strategy, hiring, and organizational structure.
Query understanding is about focusing less on the results and more on the query. It’s about figuring out what the searcher wants, rather than scoring and ranking results. Once you’ve established this mindset, your approach to search changes: you focus on query performance rather than ranking.
Presented at QConSF 2016: https://qconsf.com/sf2016/presentation/query-understanding-manifesto
I delivered this keynote at the Fast Forward Labs Data Leadership Conference on April 28, 2016. You can find related materials in the following publications:
https://www.oreilly.com/ideas/where-should-you-put-your-data-scientists
http://firstround.com/review/doing-data-science-right-your-most-common-questions-answered/
Data Science: A Mindset for Productivity
Keynote at 2015 Ronin Labs West Coast CTO Summit
https://www.eventjoy.com/e/west-coast-cto-summit-2015
Abstract
Data science isn't just about using a collection of technologies and algorithms. Data science requires a mindset that solves problems at a higher level of abstraction. How do we model utility when we think about optimization? How do we decide which hypotheses to test? How do we allocate our scarce resources to make progress?
There are no silver bullets. But I'll share what I've learned from a variety of contexts over the course of my work at Endeca, Google, and LinkedIn; and I hope you'll leave this talk with some practical wisdom you can apply to your next data science project.
My Three Ex’s: A Data Science Approach for Applied Machine LearningDaniel Tunkelang
My Three Ex’s: A Data Science Approach for Applied Machine Learning
Daniel Tunkelang (LinkedIn)
Presented at QCon San Francisco 2014 in the Applied Machine Learning and Data Science track
https://qconsf.com/presentation/my-three-ex%E2%80%99s-data-science-approach-applied-machine-learning
Abstract
This talk is about applying machine learning to solve problems.
It’s not a talk about machine learning — or at least not about the theory of machine learning. Theoretical machine learning requires a deep understanding of computer science and statistics. It’s one of the most studied areas of computer science, and advances in theoretical machine learning give us hope of solving the world’s “AI-hard” problems.
Applied machine learning is more grounded but no less important. We are surrounded by opportunities to apply classifiers, learn rules, compute similarity, and assemble clusters. We don’t need to develop new algorithms for any of these problems — our textbooks and open-source libraries have done that hard work for us.
But algorithms are not enough. Applying machine learning to solve problems requires a data science mindset that transcends the algorithmic details.
In this talk, I’ll communicate the data science mindset by describing my three ex’s: express, explain, and experiment. These three activities are the pillars of a successful strategy for applying machine learning to solve problems. Whether you’re a machine learning novice or expert, I hope you’ll leave this talk with some practical wisdom you can apply to your next project.
Web Science: How is it different?
Daniel Tunkelang, LinkedIn
Keynote Address at ACM Web Science 2014 Conference
The scientific method of observation, measurement, and experiment may be our greatest achievement as a species. The technological innovation we enjoy today is the product of a culture of systematized scientific experimentation.
But historically scientific experimentation has been expensive. Experiments consumed natural resources, took a long time to conduct, and required even more time and labor to analyze. In order to be productive, scientists have had to factor these costs into their work and to optimize accordingly.
Web science is different. Not, as some have speciously argued, because big data has made the scientific method obsolete. The key difference is that web science has changed the economics of scientific experimentation. Thus, even as web scientists apply the traditional scientific method, they optimize based on very different economics.
In this talk, I'll survey how web science has changed our approach to experimentation, for better and for worse. Specifically, I'll talk about differences in hypothesis generation, offline analysis, and online testing.
Bio
Daniel Tunkelang is Head of Query Understanding at LinkedIn, where he previously formed and led the product data science team. LinkedIn search allows members to find people, companies, jobs, groups and other content. His team aims to provide users with the best possible results that satisfy their information needs and help to get insights from professional data. Tunkelang has BS and MS degrees in computer science and math from MIT, and a PhD in computer science from CMU. He co-founded the annual symposium on human-computer interaction and information retrieval (HCIR) and wrote the first book on Faceted Search (Morgan and Claypool 2009). Prior to joining LinkedIn, Tunkelang was Chief Scientist of Endeca (acquired by Oracle in 2011 for $1.1B) and leader of the local search quality team at Google, mapping local businesses to their home pages. He is the co-inventor of 20 patents.
Better Search Through Query Understanding
Presented as a Data Talk at Intuit on April 22, 2014
Search is a fundamental problem of our time — we use search engines daily to satisfy a variety of personal and professional information needs. But search engine development still feels stuck in an information retrieval paradigm that focuses on result ranking. In this talk, I’ll advocate an emphasis on query understanding. I’ll talk about how we implement query understanding at LinkedIn, and I’ll present examples from the broader web. Hopefully you’ll come out with a different perspective on search and share my appreciation for how we can improve search through query understanding.
About the Speaker
Daniel Tunkelang leads LinkedIn's efforts around query understanding. Before that, he led LinkedIn's product data science team. He previously led a local search quality team at Google and was a founding employee of Endeca (acquired by Oracle in 2011). He has written a textbook on faceted search, and is a recognized advocate of human-computer interaction and information retrieval (HCIR). He has a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.
Keynote at CIKM 2013 Workshop on Data-driven User Behavioral Modelling and Mining from Social Media
Social Search in a Professional Context
Daniel Tunkelang (LinkedIn)
Social networks bring a new dimension to search. Instead of looking for web pages or text documents, LinkedIn members search a world of entities connected by a rich graph of relationships. Search is a fundamental part of the LinkedIn ecosystem, as it helps our members find and be found. Unlike most search applications, LinkedIn's search experience is highly personalized: two LinkedIn members performing the same search query are likely to see completely different results. Delivering the right results to the right person depends on our ability to leverage our each member's unique professional identity and network. In this talk, I'll describe the kinds of search behavior we see on LinkedIn, and some of the approaches we've taken to help our members address their information needs.
Find and be Found: Information Retrieval at LinkedInDaniel Tunkelang
Find and Be Found: Information Retrieval at LinkedIn
SIGIR 2013 Industry Track Presentation
http://sigir2013.ie/industry_track.html
LinkedIn has a unique data collection: the 200M+ members who use LinkedIn are also the most valuable entities in our corpus, which consists of people, companies, jobs, and a rich content ecosystem. Our members use LinkedIn to satisfy a diverse set of navigational and exploratory information needs, which we address by leveraging semi-structured and social content to understanding their query intent and deliver a personalized search experience. In this talk, we will discuss some of the unique challenges we face in building the LinkedIn search platform, the solutions we've developed so far, and the open problems we see ahead of us.
Shakti Sinha heads LinkedIn's search relevance team, and has been making key contributions to LinkedIn's search products since 2010. He previously worked at Google as both a research intern and a software engineer. He has an MS in Computer Science from Stanford, as well as a BS degree from College of Engineering, Pune.
Daniel Tunkelang leads LinkedIn's efforts around query understanding. Before that, he led LinkedIn's product data science team. He previously led a local search quality team at Google and was a founding employee of Endeca (acquired by Oracle in 2011). He has written a textbook on faceted search, and is a recognized advocate of human-computer interaction and information retrieval (HCIR). He has a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.
Search as Communication: Lessons from a Personal JourneyDaniel Tunkelang
Search as Communication: Lessons from a Personal Journey
by Daniel Tunkelang (Head of Query Understanding, LinkedIn)
Presented at Etsy's Code as Craft Series on May 21, 2013
When I tell people I spent a decade studying computer science at MIT and CMU, most assume that I focused my studies in information retrieval — after all, I’ve spent most of my professional life working on search.
But that’s not how it happened. I learned about information extraction as a summer intern at IBM Research, where I worked on visual query reformulation. I learned how search engines work by building one at Endeca. It was only after I’d hacked my way through the problem for a few years that I started to catch up on the rich scholarly literature of the past few decades.
As a result, I developed a point of view about search without the benefit of academic conventional wisdom. Specifically, I came to see search not so much as a ranking problem as a communication problem.
In this talk, I’ll explain my communication-centric view of search, offering examples, general techniques, and open problems.
--
Daniel Tunkelang is Head of Query Understanding at LinkedIn. Educated at MIT and CMU, he has his career working on big data, addressing key challenges in search, data mining, user interfaces, and network analysis. He co-founded enterprise search and business intelligence pioneer Endeca, where he spent a decade as its Chief Scientist. In 2011, Endeca was acquired by Oracle for over $1B. Previous to LinkedIn, he led a team at Google working on local search quality. Daniel has authored fifteen patents, written a textbook on faceted search, and created the annual symposium on human-computer interaction and information retrieval.
Enterprise Search: How do we get there from here?Daniel Tunkelang
Enterprise Search: How Do We Get There From Here?
by Daniel Tunkelang (Head of Query Understanding, LinkedIn)
Keynote at 2013 Enterprise Search Summit
We've been tackling the challenges of enterprise and site search for at least 3 decades. We've succeeded to the point that search is the gateway to many of our information repositories. Nonetheless, users of enterprise search systems are frustrated with these systems' shortcomings. We see this frustration in surveys, but, more importantly, most of us experience it personally in our daily work life. We all dream of a world where searching any information repository is as effective as searching the web—perhaps even more so. A world where we find what we're looking for, or quickly determine that it doesn't exist. Is this Utopia possible? If so, how do we get there from here? Or at least somewhere close? In this talk, Tunkelang reviews the track record of enterprise search. He talks about what's worked and what hasn't, especially as compared to web search. Finally, he proposes some paths to bring us closer to our dream.
--
Daniel Tunkelang is Head of Query Understanding at LinkedIn. Educated at MIT and CMU, he has his career working on big data, addressing key challenges in search, data mining, user interfaces, and network analysis. He co-founded enterprise search and business intelligence pioneer Endeca, where he spent a decade as its Chief Scientist. In 2011, Endeca was acquired by Oracle for over $1B. Previous to LinkedIn, he led a team at Google working on local search quality. Daniel has authored fifteen patents, written a textbook on faceted search, and created the annual symposium on human-computer interaction and information retrieval.
Big Data, We Have a Communication Problem
by Daniel Tunkelang
Presented on April 30, 2013 at the TTI/Vanguard Conference on Ginormous Systems
http://www.ttivanguard.com/conference/2013/ginormous.html
It's a cliché that we live in a world of Big Data. But the bottleneck in understanding data is not computational. Rather, the biggest challenge is designing technical solutions that effectively leverage human cognitive ability. Data analysis systems should augment people's capabilities rather than replace them. This argument is as old as computer science itself: in 1962, Doug Engelbart said that the goal of technology is “the enhancement of human intellect by increasing the capability of a human to approach a complex problem situation.” Algorithms extract signal from raw data, but people fill in the gaps, creating models and evaluating analyses.
Empowering people to understand data is not just a surface problem of building better interfaces and visualizations. We need to interact with data not only after performing computational analysis, but throughout the analysis process in order to improve our models and algorithms. In order to do so, we need tools and processes specifically designed to offer people transparency, guidance, and control.
Human-computer information retrieval has been revolutionizing our approach to information seeking -- no modern search engine limits users to black-box relevance ranking and ten blue links. We need to take similar steps in our analysis of big data, making people the center of the analysis process and developing the technical innovations that enable people to fulfill this role.
How To Interview a Data Scientist
Daniel Tunkelang
Presented at the O'Reilly Strata 2013 Conference
Video: https://www.youtube.com/watch?v=gUTuESHKbXI
Interviewing data scientists is hard. The tech press sporadically publishes “best” interview questions that are cringe-worthy.
At LinkedIn, we put a heavy emphasis on the ability to think through the problems we work on. For example, if someone claims expertise in machine learning, we ask them to apply it to one of our recommendation problems. And, when we test coding and algorithmic problem solving, we do it with real problems that we’ve faced in the course of our day jobs. In general, we try as hard as possible to make the interview process representative of actual work.
In this session, I’ll offer general principles and concrete examples of how to interview data scientists. I’ll also touch on the challenges of sourcing and closing top candidates.
Information, Attention, and Trust: A Hierarchy of NeedsDaniel Tunkelang
Presented by Daniel Tunkelang, LinkedIn Director of Data Science, at Stanford's 2nd annual conference on Computational Social Science (CSS), hosted by Institute for Research in the Social Sciences (IRiSS).
Details at https://iriss.stanford.edu/css/conference-agenda-2013
Content, Connections, and Context
Daniel Tunkelang, LinkedIn
Keynote at Workshop on Recommender Systems and the Social Web
At 6th ACM International Conference on Recommender Systems (RecSys 2012)
Recommender systems for the social web combine three kinds of signals to relate the subject and object of recommendations: content, connections, and context.
Content comes first - we need to understand what we are recommending and to whom we are recommending it in order to decide whether the recommendation is relevant. Connections supply a social dimension, both as inputs to improve relevance and as social proof to explain the recommendations. Finally, context determines where and when a recommendation is appropriate.
I'll talk about how we use these three kinds of signals in LinkedIn's recommender systems, as well as the challenges we see in delivering social recommendations and measuring their relevance.
Keynote at 2012 Semantic Technology and Business Conference
Scale, Structure, and Semantics
Daniel Tunkelang, LinkedIn
Science fiction has a mixed track record when it comes to anticipating technological innovations. While Jules Verne fared well with with his predictions of submarine and space technology, artificial intelligence hasn't produced anything like Arthur C. Clarke's HAL 9000.
Instead, we've managed to elicit intelligence from machines through unexpected means. Search engines have achieved remarkable success in organizing the world's information by crawling the web, indexing documents, and exploiting link structure to establish authoritativeness. At LinkedIn, we apply large-scale analytics to terabytes of semistructured data to deliver products and insights that serve our 150M+ members. Semantics emerge when we apply the right analytical techniques to a sufficient quality and quantity of data.
In this talk, I will describe how LinkedIn's huge and rich graph of relationship data that powers the products our users love. I believe that the lessons we have learned apply broadly to other semantic applications. While quantity and quality of data are the key challenges to delivering a semantically rich experience, the key is to create the right ecosystem that incents people to give you good data, which then forms the basis for great data products.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
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.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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/
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
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During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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.
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
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
8. Identity Connect, find and be found Rolodex, Resume, Business Card LinkedIn Profile, Address Book, Search Insights Be great at what you do Newspapers, Trade Magazines, Events Homepage, LinkedIn Today, Groups Everywhere Desktop Work wherever our members work Mobile, APIs, Plug-Ins What is LinkedIn? 3
12. Insights: Power of Aggregation Before employees worked at Yahoo! (169) Google (96) Oracle (78) Microsoft (72) IBM (43) Before employees worked at Google(475) Microsoft (448) LinkedIn (169) Apple, Inc. (154) ebay(133) 7
42. People Search: HMM + Segmentation for i in [1..n] s w1 w2 … wi if Pc(s) > 0 a new Segment() a.segs {s} a.prob Pc(s) B[i] {a} for j in [1..i-1] for b in B[j] s wjwj+1 … wi if Pc(s) > 0 a new Segment() a.segsb.segs U {s} a.probb.prob * Pc(s) B[i] B[i] U {a} sort B[i] by prob truncate B[i] to size k 25
54. … industry description functional area Current Position title summary tenure length industry functional area … Job Matching: Algorithm Corpus Stats Job Matching Transition probabilities Connectivity yrs of experience to reach title education needed for this title … Binary Exact matches: geo, industry, … Soft transition probabilities, similarity, … Text title geo company User Base Similarity (candidate expertise, job description) 0.56 Filtered Similarity (candidate specialties, job description) 0.2 Candidate Transition probability (candidate industry, job industry) 0.43 General expertise specialties education headline geo experience Title Similarity 0.8 Similarity (headline, title) 0.7 derived . . . 30
LinkedIn is all about connecting professionals with opportunity. We are taking the tools that made people successful in the 20th century and bringing them into the 21st century.