Semantic Equivalence of e-Commerce Queries
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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).
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Machine Learning Interpretability - Mateusz Dymczyk - H2O AI World London 2018
More from Daniel Tunkelang
MMM, Search!
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
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: A Manifesto
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
Where should you put your data scientists?
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
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 Learning
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?
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
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.
Social Search in a Professional Context
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 LinkedIn
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 Journey
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?
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
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
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 Needs
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
Data By The People, For The People
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.
Content, Connections, and Context
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.
Scale, Structure, and Semantics
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.
Strata 2012: Humans, Machines, and the Dimensions of Microwork
Presentation from O'Reilly Strata 2012 on Big Data
Humans, Machines, and the Dimensions of Microwork
Daniel Tunkelang (LinkedIn)
Claire Hunsaker (Samasource)
The advent of crowdsourcing has wildly expanded the ways we think of incorporating human judgments into computational workflows. Computer scientists, economists, and sociologists have explored how to effectively and efficiently distribute microwork tasks to crowds and use their work as inputs to create or improve data products. Simultaneously, crowdsourcing providers are exploring the bounds of mechanical QA flows, worker interfaces, and workforce management systems.
But what tasks should be performed by humans rather than algorithms? And what makes a set of human judgments robust? Quantity? Consensus? Quality or trustworthiness of the workers? Moreover, the robustness of judgments depends not only on the workers, but on the task design. Effective crowdsourcing is a cooperative endeavor.
In this talk, we will analyze various dimensions of microwork that characterize applications, tasks, and crowds. Drawing on our experience at companies that have pioneered the use of microwork (Samasource) and data science (LinkedIn), we will offer practical advice to help you design crowdsourcing workflows to meet your data product needs.
Recommendations as a Conversation with the User
These slides are from a tutorial at the 5th ACM International Conference on Recommender Systems (RecSys 2011).
Recommender systems aim to provide users with products or content that satisfy the users' stated or inferred needs. The primary evaluation measures for recommender systems emphasize either the perceived relevance of the recommendations or the actions associated with those recommendations (e.g., purchases or clicks). Unfortunately, this transactional emphasis neglects how users interact with recommendations in the context of information seeking tasks. The effectiveness of this interaction determines the user's experience beyond a single transaction. This tutorial explores the role of recommendations as part of a conversation between the user and an information seeking system. The tutorial does not require any special background in interfaces or usability, and will focus on practical techniques to make recommender systems most effective for users.
More from Daniel Tunkelang (20)
My Three Ex’s: A Data Science Approach for Applied Machine Learning
My Three Ex’s: A Data Science Approach for Applied Machine Learning
Find and be Found: Information Retrieval at LinkedIn
Find and be Found: Information Retrieval at LinkedIn
Search as Communication: Lessons from a Personal Journey
Search as Communication: Lessons from a Personal Journey
Information, Attention, and Trust: A Hierarchy of Needs
Information, Attention, and Trust: A Hierarchy of Needs
Strata 2012: Humans, Machines, and the Dimensions of Microwork
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Semantic Equivalence of e-Commerce Queries
- 1. Semantic Equivalence of e-Commerce Queries Aritra Mandal and Daniel Tunkelang and Zhe Wu eBay Inc.
- 2. Search query != search intent. ● Information retrieval researchers worry about queries that map to multiple intents. jaguar or ? ● Practitioners worry more about multiple queries that map to the same intent. lightning to 3.5mm iphone to aux
- 3. Equivalent queries should yield equivalent experiences. Recall? CTR? Conversion Rate? ... ? or
- 4. Opportunity to increase recall while preserving precision. Similar but not equivalent intent.
- 5. High-level strategy to leverage query equivalence. Map queries to vectors. Store in nearest-neighbor database. (i.e., optimize for user or business outcome)
- 6. Two strategies for recognizing equivalent queries. ● Surface Similarity ○ Variation in inflection, word order, compounding, noise words. black tshirts for men = mens black t-shirt = ● Behavioral Similarity ○ Queries lead to engagement with equivalent or similar results. lightning to 3.5mm = iphone to aux =
- 7. Introducing the “bag of documents” model.
- 8. Query vectors are centroids of associated product vectors ► ► [0.13, 0.81, … ] [0.09, 0.75, … ] … ► [0.11, 0.79, … ] [0.13, 0.81, … ] [0.09, 0.77, … ] … ► [0.12, 0.78, … ] ► cos > 0.98 black tshirts for men mens black t-shirt
- 9. Works well, but only for head and torso queries. ● Offline approach works for queries with enough engagement history. ● Would be expensive to compute aggregates of result vectors online. ● Still, head and torso queries tend to represent a large fraction of traffic.
- 10. Train online sentence transformer model for tail queries. ● Train using (query1, query2, similarity) triples from offline model. ● Oversample similar query pairs to increase sensitivity where it matters. ● Fine-tune a pre-trained micro-BERT sentence transformer model. ● Concatenate the output of a query classifier to the query keywords.
- 11. Architecture for Online Query Similarity Model
- 12. Results Model Dataset Name Pearson’s correlation query-sim-ecom eBay Internal 0.87 query-sim-ecom ESCI query-query 0.85 all-MiniLM-L12-v2 ESCI query-query 0.68 Query 1 Query 2 cosine hdmi to galaxy s8 s9 hdmi 0.9993 movie money prop money 0.9995 cassette adapter for iphone tape to aux 0.9993 Examples from ESCI of queries with low surface but high behavioral similarity:
- 13. Summary ● Queries with equivalent intent should yield equivalent experiences. ● Query similarity can increase recall while preserving precision. ● Signals can come from either surface or behavioral similarity. ● Offline bag-of-documents model: queries as means of product vectors. ● Fine-tune online Micro-BERT sentence transformer model for tail queries. ● It just works!