Zillow is a real estate marketplace that uses data science and machine learning across many areas of its business. It has a large housing database with over 100 million homes and uses algorithms like the Zestimate to provide home valuations. Computer vision models analyze photos and videos to estimate home qualities. Recommendation systems suggest relevant homes to users based on their past activities. Zillow aims to attract users with data-driven products and generate revenue by connecting professionals to customers. Data science is crucial to Zillow's business and it continues hiring more data scientists and engineers.
This document provides an overview of data science work at Zillow. It discusses Zillow's use of machine learning models like the Zestimate and Rent Zestimate to analyze housing data. It describes Zillow's technology stack, which heavily leverages Python, R, and SQL. Specific examples are provided on automated waterfront determination using GIS data and discovering home street features. The document also discusses how tools like Dato and Scikit-Learn are used for tasks like fraud detection, property matching, and data modeling. In closing, current job openings at Zillow are listed.
Startup Secrets - Game Changing Business ModelsMichael Skok
In our industry, it’s not uncommon for entrepreneurs to become so mono-focused on the novelty of their product that they forget to innovate sufficiently around their business model. A disruptive business model can be at least as important as a discontinuous innovation.
Using Redis Streams To Build Event Driven Microservices And User Interface In...Redis Labs
The document summarizes Bobby Calderwood's presentation on using Redis Streams to build event-driven microservices and user interfaces in Clojure(Script). The presentation covers how Redis Streams were used to facilitate asynchronous processing and distributed consistency for a customer project. It also discusses how Carmine, the Clojure Redis client, was updated to support Redis Streams shortly after their release. The presentation concludes with a demo of how Redis Streams can be used to retrofit an existing system with asynchronous integration.
Big Data Business Wins: Real-time Inventory Tracking with HadoopDataWorks Summit
MetaScale is a subsidiary of Sears Holdings Corporation that provides big data technology solutions and services focused on Hadoop. It helped Sears implement a real-time inventory tracking system using Hadoop and Cassandra to create a single version of inventory data across different legacy systems. This allowed inventory levels to be updated in real-time from POS data, reducing out-of-stocks and improving the customer experience.
This document provides an overview of data science work at Zillow. It discusses Zillow's use of machine learning models like the Zestimate and Rent Zestimate to analyze housing data. It describes Zillow's technology stack, which heavily leverages Python, R, and SQL. Specific examples are provided on automated waterfront determination using GIS data and discovering home street features. The document also discusses how tools like Dato and Scikit-Learn are used for tasks like fraud detection, property matching, and data modeling. In closing, current job openings at Zillow are listed.
Startup Secrets - Game Changing Business ModelsMichael Skok
In our industry, it’s not uncommon for entrepreneurs to become so mono-focused on the novelty of their product that they forget to innovate sufficiently around their business model. A disruptive business model can be at least as important as a discontinuous innovation.
Using Redis Streams To Build Event Driven Microservices And User Interface In...Redis Labs
The document summarizes Bobby Calderwood's presentation on using Redis Streams to build event-driven microservices and user interfaces in Clojure(Script). The presentation covers how Redis Streams were used to facilitate asynchronous processing and distributed consistency for a customer project. It also discusses how Carmine, the Clojure Redis client, was updated to support Redis Streams shortly after their release. The presentation concludes with a demo of how Redis Streams can be used to retrofit an existing system with asynchronous integration.
Big Data Business Wins: Real-time Inventory Tracking with HadoopDataWorks Summit
MetaScale is a subsidiary of Sears Holdings Corporation that provides big data technology solutions and services focused on Hadoop. It helped Sears implement a real-time inventory tracking system using Hadoop and Cassandra to create a single version of inventory data across different legacy systems. This allowed inventory levels to be updated in real-time from POS data, reducing out-of-stocks and improving the customer experience.
This document provides information from a May 22, 2014 capital markets day presentation by Kinnevik Rocket Capital. It summarizes the business model and growth of Linio, an e-commerce marketplace targeting Spanish-speaking Latin America. Key points include:
- Linio addresses the large and fast-growing retail market in Latin America, with over 225 million potential customers across 4 countries initially.
- It has become the clear market leader in online retail in all countries it currently operates - Mexico, Colombia, Peru and Venezuela.
- Sales and customer base have grown exponentially since launch, demonstrating the huge potential as e-commerce penetration in the region remains low at around 1% of total retail.
Graph Structure in the Web - Revisited. WWW2014 Web Science TrackChris Bizer
The document discusses research that revisits the graph structure of the web using a new large crawl from Common Crawl. It finds that the web has become more dense and connected over time, with the largest strongly connected component growing significantly. While previous research found power laws for in- and out-degrees, this data does not fit power laws and instead has heavy-tailed distributions. The shape of the bow-tie structure also depends on the specific crawl used. The authors provide the new crawl data and analysis to enable further research on the evolving structure of the web graph.
The document describes how to import JSON data into Neo4j using Cypher and APOC. It provides examples of JSON data exported from a Neo4j database containing movie data. It then shows the Cypher queries used to export the data to JSON files and the steps to import the JSON data back into a Neo4j database, including loading nodes and relationships and handling optional properties.
Uber Business Metrics Generation and Management Through Apache FlinkWenrui Meng
Uber uses Apache Flink to generate and manage business metrics in real-time from raw streaming data sources. The system defines metrics using a domain-specific language and optimizes an execution plan to generate the metrics directly rather than first generating raw datasets. This avoids inefficiencies, inconsistencies, and wasted resources. The system provides a unified way to define metrics from multiple data sources and store results in various databases and warehouses.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
TELUS is introducing a new Network as a Service (NaaS) solution that provides a secure and reliable SD-WAN. The key benefits of TELUS NaaS include:
1) It is cloud-optimized, allowing customers to directly access cloud applications from branch offices to save time and increase productivity.
2) NaaS provides security through integrated next-generation firewalls and reliability with automatic LTE backup if the primary connection fails.
3) The solution is easy to use with a self-serve portal that reduces deployment time by 80% and allows for simple management and monitoring of the network.
4) TELUS NaaS offers cost savings of up to 67
Network effects. It’s one of the most important concepts for business in general and especially for tech businesses, as it’s the key dynamic behind many successful software-based companies. Understanding network effects not only helps build better products, but it helps build moats and protect software companies against competitors’ eating away at their margins.
Yet what IS a network effect? How do we untangle the nuances of 'network effects' with 'marketplaces' and 'platforms'? What’s the difference between network effects, virality, supply-side economies of scale? And how do we know a company has network effects?
Most importantly, what questions can entrepreneurs and product managers ask to counter the wishful thinking and sometimes faulty assumption behind the belief that “if we build it, they will come” … and instead go about more deterministically creating network effects in their business? Because it's not a winner-take-all market by accident.
Metaverse - The Future of Marketing and Web 3.0.pdfthetechnologynews
The global metaverse market was valued at USD 107,100.67 Million in 2020, and it is expected to reach a value of USD 758,600.86 Million by 2027, at a CAGR of 37.1% over the forecast period (2020 - 2027).
Get To Know More : https://skyquestt.com/report/metaverse-market
The Metaverse is a virtual interactive self-sufficient ecosystem comprising mobile networks, augmented reality, social media, gaming, virtual reality, e-commerce, cryptocurrency, and workplace. This universe is envisioned as the internet's future, bringing together augmented reality (AR), virtual reality (VR), and physical worlds in a common digital arena. NFTs and online events are exploding, opening up a world of possibilities for the metaverse and associated technologies.
The transition to the Metaverse is fast approaching. Several components and features of this open-source platform have progressed to the point where they may be smoothly merged to investigate the idea of building a parallel virtual reality. NFTs and online events are exploding, opening up a world of possibilities for the metaverse.
Global Metaverse Market Segmental Analysis
The Global Metaverse Market is segmented based on Type, Technology, and Application. Based on Type it is categorized into: Mobile and Desktop. Based on Technology it is categorized into: Blockchain, VR & AR, Mixed Reality, and Others. Based on Application it is categorized into: Gaming, Online Shopping, Content Creation, Social Media, and Others. Based on region it is categorized into: North America, Europe, Asia-Pacific, South America, and MEA.
Analysis by Application
The gaming segment is expected to be the largest segment in the Metaverse market throughout the forecast period (2020-2027).Due to major ongoing innovations and advances by developers, as well as a rising focus on improving immersion and making games more realistic, the gaming segment will have the leading revenue share of more than 25% in 2021. Furthermore, corporations' growing emphasis on using games to enhance their corporate image is expected to drive revenue growth.
China, the world's second-largest economy, is expected to reach a market size of USD 103,100.26 million in 2026, with a CAGR of 38.1 % throughout the forecast period. Other notable global markets include Japan and Canada, which are expected to increase at 31.3% and 29.6%, respectively, throughout the forecast period. Germany is expected to develop at a 36.8% CAGR within Europe, while the rest of the European market would reach USD 59,500.67 Million by the conclusion of the forecast period.
Condi is developing an end-to-end marketplace that allows people to purchase fractional shares of vacation homes, gaining ownership benefits without the hassle and costs of whole home ownership. By purchasing just 1/10th of a home for around $16,500 down, owners can enjoy 5 weeks of usage per year while Condi handles property management, scheduling, and financing. The platform aims to provide a more affordable and flexible alternative to traditional timeshares and whole home ownership through a shared equity model.
Netflix is the world’s leading Internet television network with over 48 million members in more than 40 countries enjoying more than one billion hours of TV shows and movies per month, including original series. Netflix uses machine learning to deliver a personalized experience to each one of our 48 million users.
In this talk you will hear about the machine learning algorithms that power almost every part of the Netflix experience, including some of our recent work on distributed Neural Networks on AWS GPUs. You will also get an insight into the innovation approach that includes offline experimentation and online AB testing. Finally, you will learn about the system architectures that enable all of this at a Netflix scale.
Verloop.io provides conversational AI solutions to enable brands to build delightful customer support experiences. Their voice and chatbot AI solutions can handle a high volume of customer queries, reducing the workload on agents by 80% while providing 24/7 support in over 20 languages. Verloop.io has powered customer support for over 200 brands across industries such as banking, insurance, retail, and education.
Learn how to use storytelling techniques to build powerful account nurturing systems. Leverage age old techniques only master marketers use to lead people through to the new world of using your products instead of the old world and old products.
Talk on Apache Kudu, presented by Asim Jalis at SF Data Engineering Meetup on 2/23/2016.
http://www.meetup.com/SF-Data-Engineering/events/228293610/
Big Data applications need to ingest streaming data and analyze it. HBase is great at ingesting streaming data but not good at analytics. HDFS is great at analytics but not at ingesting streaming data. Frequently applications ingest data into HBase and then move it to HDFS for analytics. What if you could use a single system for both use cases?
What if you could use a single system for both use cases? This could dramatically simplify your data pipeline architecture.
This is where Kudu comes in. Kudu is a storage system that lives between HDFS and HBase. It is good at both ingesting streaming data and good at analyzing it using Spark, MapReduce, and SQL.
WebHopers Company Profile | Best Digital Marketing & Web Development CompanyMohit WebHopers
The document discusses the benefits of meditation for reducing stress and anxiety. Regular meditation practice can help calm the mind and body by lowering heart rate and blood pressure. Making meditation a part of a daily routine, even if just 10-15 minutes per day, can have mental and physical health benefits over time by helping people feel more relaxed and better able to handle life's stresses.
The 3DO gaming console failed for several key reasons:
1) It was priced too high at $700 when it launched, much more expensive than competitors like Sega and Nintendo.
2) It lacked a large library of games at launch, giving customers little incentive to purchase the new console.
3) 3DO's licensing model meant it did not have full control over the hardware production and pricing, putting it at a disadvantage against competitors who owned the whole production process.
At Zillow, we calculate a Zestimate® home value for about 100 million homes nationwide daily. But between batch runs, users could update their home facts or even list their home on the market. Housing markets move fast, and we want Zestimates to reflect the latest state of our housing data. In this talk, I will present the architecture of the Zestimate and the infrastructure powering it. Inspired by Lambda Architecture, the Zestimate relies on both a near real-time and a batch component. I will highlight how the design allows us to be nimble in the face of data changes, while not sacrificing algorithmic accuracy during daily batch runs.
This document describes a web service that analyzes web crawl data to provide contextual information about locations. It extracts topics like weather, healthcare, crime, and employment that are relevant to a given location from common crawl data stored on Amazon S3. The system uses Apache Pig on a Hadoop cluster to analyze the data, builds an index of locations to associated words, and makes the results searchable through Elastic Search. It aims to provide useful information to people moving to new places, policy makers, journalists, and researchers.
Netflix: Digital Marketing Evaluation of the Over-the-top Media-Service ProviderSagarChaujar
Netflix: Digital Marketing Evaluation of one of the World's Biggest Media Service Provider with its Social Media Strategies & Consumer Insights/Sentiments on the campaigns they run on the Internet.
Neighborhood Match is a service that helps people relocating to a new city find the perfect neighborhood based on their needs. It leverages data and partnerships with real estate companies to provide personalized recommendations. The founders have experience from major tech companies and see an opportunity in the $9.2 billion realtor advertising market. They are currently refining their prototype and business model based on customer feedback.
The document provides information about QuikrHomes, a real estate marketing company in India. It details the services QuikrHomes provides, including real estate research and data collection across various Indian cities. Key services highlighted include project performance analysis, demand and supply analysis at the city and micro-market level, and consumer surveys. The document also describes QuikrHomes' methodology for collecting and auditing real estate data from various sources.
This document provides information from a May 22, 2014 capital markets day presentation by Kinnevik Rocket Capital. It summarizes the business model and growth of Linio, an e-commerce marketplace targeting Spanish-speaking Latin America. Key points include:
- Linio addresses the large and fast-growing retail market in Latin America, with over 225 million potential customers across 4 countries initially.
- It has become the clear market leader in online retail in all countries it currently operates - Mexico, Colombia, Peru and Venezuela.
- Sales and customer base have grown exponentially since launch, demonstrating the huge potential as e-commerce penetration in the region remains low at around 1% of total retail.
Graph Structure in the Web - Revisited. WWW2014 Web Science TrackChris Bizer
The document discusses research that revisits the graph structure of the web using a new large crawl from Common Crawl. It finds that the web has become more dense and connected over time, with the largest strongly connected component growing significantly. While previous research found power laws for in- and out-degrees, this data does not fit power laws and instead has heavy-tailed distributions. The shape of the bow-tie structure also depends on the specific crawl used. The authors provide the new crawl data and analysis to enable further research on the evolving structure of the web graph.
The document describes how to import JSON data into Neo4j using Cypher and APOC. It provides examples of JSON data exported from a Neo4j database containing movie data. It then shows the Cypher queries used to export the data to JSON files and the steps to import the JSON data back into a Neo4j database, including loading nodes and relationships and handling optional properties.
Uber Business Metrics Generation and Management Through Apache FlinkWenrui Meng
Uber uses Apache Flink to generate and manage business metrics in real-time from raw streaming data sources. The system defines metrics using a domain-specific language and optimizes an execution plan to generate the metrics directly rather than first generating raw datasets. This avoids inefficiencies, inconsistencies, and wasted resources. The system provides a unified way to define metrics from multiple data sources and store results in various databases and warehouses.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
TELUS is introducing a new Network as a Service (NaaS) solution that provides a secure and reliable SD-WAN. The key benefits of TELUS NaaS include:
1) It is cloud-optimized, allowing customers to directly access cloud applications from branch offices to save time and increase productivity.
2) NaaS provides security through integrated next-generation firewalls and reliability with automatic LTE backup if the primary connection fails.
3) The solution is easy to use with a self-serve portal that reduces deployment time by 80% and allows for simple management and monitoring of the network.
4) TELUS NaaS offers cost savings of up to 67
Network effects. It’s one of the most important concepts for business in general and especially for tech businesses, as it’s the key dynamic behind many successful software-based companies. Understanding network effects not only helps build better products, but it helps build moats and protect software companies against competitors’ eating away at their margins.
Yet what IS a network effect? How do we untangle the nuances of 'network effects' with 'marketplaces' and 'platforms'? What’s the difference between network effects, virality, supply-side economies of scale? And how do we know a company has network effects?
Most importantly, what questions can entrepreneurs and product managers ask to counter the wishful thinking and sometimes faulty assumption behind the belief that “if we build it, they will come” … and instead go about more deterministically creating network effects in their business? Because it's not a winner-take-all market by accident.
Metaverse - The Future of Marketing and Web 3.0.pdfthetechnologynews
The global metaverse market was valued at USD 107,100.67 Million in 2020, and it is expected to reach a value of USD 758,600.86 Million by 2027, at a CAGR of 37.1% over the forecast period (2020 - 2027).
Get To Know More : https://skyquestt.com/report/metaverse-market
The Metaverse is a virtual interactive self-sufficient ecosystem comprising mobile networks, augmented reality, social media, gaming, virtual reality, e-commerce, cryptocurrency, and workplace. This universe is envisioned as the internet's future, bringing together augmented reality (AR), virtual reality (VR), and physical worlds in a common digital arena. NFTs and online events are exploding, opening up a world of possibilities for the metaverse and associated technologies.
The transition to the Metaverse is fast approaching. Several components and features of this open-source platform have progressed to the point where they may be smoothly merged to investigate the idea of building a parallel virtual reality. NFTs and online events are exploding, opening up a world of possibilities for the metaverse.
Global Metaverse Market Segmental Analysis
The Global Metaverse Market is segmented based on Type, Technology, and Application. Based on Type it is categorized into: Mobile and Desktop. Based on Technology it is categorized into: Blockchain, VR & AR, Mixed Reality, and Others. Based on Application it is categorized into: Gaming, Online Shopping, Content Creation, Social Media, and Others. Based on region it is categorized into: North America, Europe, Asia-Pacific, South America, and MEA.
Analysis by Application
The gaming segment is expected to be the largest segment in the Metaverse market throughout the forecast period (2020-2027).Due to major ongoing innovations and advances by developers, as well as a rising focus on improving immersion and making games more realistic, the gaming segment will have the leading revenue share of more than 25% in 2021. Furthermore, corporations' growing emphasis on using games to enhance their corporate image is expected to drive revenue growth.
China, the world's second-largest economy, is expected to reach a market size of USD 103,100.26 million in 2026, with a CAGR of 38.1 % throughout the forecast period. Other notable global markets include Japan and Canada, which are expected to increase at 31.3% and 29.6%, respectively, throughout the forecast period. Germany is expected to develop at a 36.8% CAGR within Europe, while the rest of the European market would reach USD 59,500.67 Million by the conclusion of the forecast period.
Condi is developing an end-to-end marketplace that allows people to purchase fractional shares of vacation homes, gaining ownership benefits without the hassle and costs of whole home ownership. By purchasing just 1/10th of a home for around $16,500 down, owners can enjoy 5 weeks of usage per year while Condi handles property management, scheduling, and financing. The platform aims to provide a more affordable and flexible alternative to traditional timeshares and whole home ownership through a shared equity model.
Netflix is the world’s leading Internet television network with over 48 million members in more than 40 countries enjoying more than one billion hours of TV shows and movies per month, including original series. Netflix uses machine learning to deliver a personalized experience to each one of our 48 million users.
In this talk you will hear about the machine learning algorithms that power almost every part of the Netflix experience, including some of our recent work on distributed Neural Networks on AWS GPUs. You will also get an insight into the innovation approach that includes offline experimentation and online AB testing. Finally, you will learn about the system architectures that enable all of this at a Netflix scale.
Verloop.io provides conversational AI solutions to enable brands to build delightful customer support experiences. Their voice and chatbot AI solutions can handle a high volume of customer queries, reducing the workload on agents by 80% while providing 24/7 support in over 20 languages. Verloop.io has powered customer support for over 200 brands across industries such as banking, insurance, retail, and education.
Learn how to use storytelling techniques to build powerful account nurturing systems. Leverage age old techniques only master marketers use to lead people through to the new world of using your products instead of the old world and old products.
Talk on Apache Kudu, presented by Asim Jalis at SF Data Engineering Meetup on 2/23/2016.
http://www.meetup.com/SF-Data-Engineering/events/228293610/
Big Data applications need to ingest streaming data and analyze it. HBase is great at ingesting streaming data but not good at analytics. HDFS is great at analytics but not at ingesting streaming data. Frequently applications ingest data into HBase and then move it to HDFS for analytics. What if you could use a single system for both use cases?
What if you could use a single system for both use cases? This could dramatically simplify your data pipeline architecture.
This is where Kudu comes in. Kudu is a storage system that lives between HDFS and HBase. It is good at both ingesting streaming data and good at analyzing it using Spark, MapReduce, and SQL.
WebHopers Company Profile | Best Digital Marketing & Web Development CompanyMohit WebHopers
The document discusses the benefits of meditation for reducing stress and anxiety. Regular meditation practice can help calm the mind and body by lowering heart rate and blood pressure. Making meditation a part of a daily routine, even if just 10-15 minutes per day, can have mental and physical health benefits over time by helping people feel more relaxed and better able to handle life's stresses.
The 3DO gaming console failed for several key reasons:
1) It was priced too high at $700 when it launched, much more expensive than competitors like Sega and Nintendo.
2) It lacked a large library of games at launch, giving customers little incentive to purchase the new console.
3) 3DO's licensing model meant it did not have full control over the hardware production and pricing, putting it at a disadvantage against competitors who owned the whole production process.
At Zillow, we calculate a Zestimate® home value for about 100 million homes nationwide daily. But between batch runs, users could update their home facts or even list their home on the market. Housing markets move fast, and we want Zestimates to reflect the latest state of our housing data. In this talk, I will present the architecture of the Zestimate and the infrastructure powering it. Inspired by Lambda Architecture, the Zestimate relies on both a near real-time and a batch component. I will highlight how the design allows us to be nimble in the face of data changes, while not sacrificing algorithmic accuracy during daily batch runs.
This document describes a web service that analyzes web crawl data to provide contextual information about locations. It extracts topics like weather, healthcare, crime, and employment that are relevant to a given location from common crawl data stored on Amazon S3. The system uses Apache Pig on a Hadoop cluster to analyze the data, builds an index of locations to associated words, and makes the results searchable through Elastic Search. It aims to provide useful information to people moving to new places, policy makers, journalists, and researchers.
Netflix: Digital Marketing Evaluation of the Over-the-top Media-Service ProviderSagarChaujar
Netflix: Digital Marketing Evaluation of one of the World's Biggest Media Service Provider with its Social Media Strategies & Consumer Insights/Sentiments on the campaigns they run on the Internet.
Neighborhood Match is a service that helps people relocating to a new city find the perfect neighborhood based on their needs. It leverages data and partnerships with real estate companies to provide personalized recommendations. The founders have experience from major tech companies and see an opportunity in the $9.2 billion realtor advertising market. They are currently refining their prototype and business model based on customer feedback.
The document provides information about QuikrHomes, a real estate marketing company in India. It details the services QuikrHomes provides, including real estate research and data collection across various Indian cities. Key services highlighted include project performance analysis, demand and supply analysis at the city and micro-market level, and consumer surveys. The document also describes QuikrHomes' methodology for collecting and auditing real estate data from various sources.
Why We Started This Company
We had a bad roommate, who hasn't? Currently, there was no simple way to find, vet, and match roommates in the market today, so we set out to make that happen and Roommatefax.com is what we came up with.
What Sets Us Apart
There are 5 important factors to consider in the roommate search. 1) Would we get along? 2) If we did get along, do we like/dislike the same things? 3) Is the potential roommate who they say or appear to be? 4) Is there an easy way to find a person to live with like this? 5) Is there an easy way to find a place. Although some companies answer one or two of these questions with their service, none of them answer all 5 in an easy to use site/app at a competitive price.
This document discusses a personalization platform that uses A/B testing and big data to personalize the visitor experience on websites. It claims this can increase conversions by up to 40%. The platform segments visitors, predicts their behavior, and acts by showing tailored content to improve outcomes. It is a full-featured solution for marketers to optimize websites across devices. The company aims to partner with resellers, agencies, and publishers to build its business globally.
The document summarizes the real estate services offered by Leslie Ebersole and Katie Hemming of Baird & Warner to sell Sylvia and Richard Warner's home located at 3N618 Trotter Lane. It outlines their full marketing strategy including listing the home on multiple online sites, open houses, broker tours, and direct mail campaigns. It also discusses home staging, pricing strategy, and providing regular updates to guide the home through the selling process.
Zillow + Optimizely: Building the Bridge to $20 Billion RevenueOptimizely
Join Jason Tabert, Senior CRO Marketing Specialist, and learn how Zillow is using Optimizely’s experimentation, personalization and integrations to help grow their revenue to $20 billion by helping their customers cross the real estate chasm from despair to delight.
PeppyWalls is an online portal for rental homes in India that exclusively lists handpicked and standardized homes. It was launched in Bangalore in 2015. The company aims to build trust in the rental homes segment and set benchmarks for home standardization and rental. PeppyWalls addresses issues like lack of standardized homes, rental losses from poor maintenance, and the need for homeseekers to personally visit properties. It utilizes technology and home services partners to standardize listings. PeppyWalls has over 1200 listings in Bangalore and seeks funding to expand operations and standardize more homes and PG accommodations.
Internet Marketing, EO Accelerator PresentationTop Draw Inc.
Top Draw is an online advertising agency that has been in business since 1995. They help clients connect with customers through effective website design and internet marketing strategies. This presentation discusses why internet marketing is important, provides practical examples of how businesses have benefited, and gives an overview of key tactics like search engine optimization, pay-per-click advertising, local search, mobile strategies, and analytics. The presentation emphasizes measuring results and provides actionable steps attendees can take to improve their online presence and lead generation.
GAMING INDUSTRY ENABLER. WE PROVIDE WHITE LABEL & CUSTOM GAMES PLATFORMSGameZBoost
GAMING INDUSTRY ENABLER. WE PROVIDE WHITE LABEL & CUSTOM GAMES PLATFORMS. THESE ARE FULLY FEATURED AND HIGHLY CUSTOMIZABLE PLATFORMS FOR WEBSITES, MOBILE & SOCIAL MEDIA.
WE OFFER GAME INTEGRATION SOLUTIONS FOR PUBLISHERS ACROSS THE GLOBE. OUR PLATFORMS ARE ALREADY RUNNING GAMES SITES FOR SOME OF THE WORLD’S TOP PUBLISHERS, IN 16 COUNTRIES AND 12 LANGUAGES.
GAME STUDIOS CAN TAP INTO AN INSTANT AUDIENCE BY INTEGRATING WITH OUR PLATFORM.
How do you win the loyalty of the modern renter?
We’re sharing the insights from Calzadilla and Kirkpatrick’s presentation with you so you too can learn how to win the loyalty of today’s consumer in a fast-paced, mobile world.
6 Top Real Estate Managed Analytics Service Providers.pptxKavika Roy
Data analytics and predictive analytics play vital roles in the real estate industry. Agencies and brokers can opt for managed analytics services to access real-time actionable insights and make faster decisions. Here, we’ll discuss the top Managed Analytics Service Providers in Real Estate
The global real estate industry is constantly evolving, be it an increasing demand for residential property in the suburbs or the faster adoption of technology to automate underwriting and speed up sales.
Reocon social media power of lead generation_1-29-2012Ken Blevins
Ken Blevins presented on using social media and internet marketing for lead generation. He defined social media as online tools that promote sharing and conversations to engage customers and drive business. Blevins stated that using Craigslist as a lead generation tool is more effective than just listing properties. He recommended creating targeted campaigns, call-to-action pages to capture leads, testing different ad elements, and automating email follow up to prospects. The presentation provided strategies for posting, tracking, and optimizing Craigslist ads to generate buyer leads.
TripleLift: Preparing for a New Programmatic Ad-Tech WorldVoltDB
Michael Harroun, Director of Backend Architecture at TripleLift explores the benefits of leveraging real-time databases to power their programmatic native advertisement exchange.
Ben Wright of Atlas Advertising presents What Would Google Do if it Were in Charge of Economic Development? At the Arkansas Economic Developers Conference in Hot Springs, Arkansas
ZingClick is a firm focused on meeting the technological demands of the public, starting from websites to mobile apps to conceptualization of your ideas. We excel in providing solutions that are unique, innovative, and effective, ensuring it shouts out the message you want to convey.
Think of us as your “business basket” – we cater to your every technological needs. Our modus operandi has the main essence as uniqueness. None of our works will be remotely same. While you focus on your business, we work to deliver you the branding that your company deserves ensuring that your popularity entices your potential customers thereby crafting solutions to meet your current needs while creating a solid foundation for future growth.
Since there is a huge upsurge in technologies, we make it a point to stay up to date with it. Our talented panel of developers provide you edge cutting designs for your Mobile and Website Solutions. If you are one the lookout for people who will provide you clearer view of your idea – Conceptualization staff at your service, sir! Need to make it big in the virtual world? Let us in for guaranteed branding of your website via our experts in SEO techniques. We also specialize in creative designing a.k.a. logos, websites and wapsites.
Contact us for all your technical related demands or queries and see how well we can fulfill your needs.
This document compares advertising exchanges to Wall Street exchanges and stock markets. It notes that while Wall Street has strict listing requirements and regulations for traded assets, advertising exchanges have no listing requirements or monitoring of traded impressions. Unlike stocks, impressions are not fixed assets and expire quickly. It also notes that while rating agencies evaluate financial instruments, there is a lack of transparency around methodologies for evaluating digital advertising. The conclusion is that just as one should diversify financial investments, advertisers should benchmark exchanges to ensure performance and not rely on any single platform or product.
Similar to Overview of Data Science at Zillow (20)
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
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With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
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When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
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The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
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- The top challenges for privacy leaders, practitioners, and organizations in 2024
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Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
10. 10
Zillow Group’s audience continues to grow
MONTHLY UNIQUE USERS
Quarterly average (Millions)
0
20
40
60
80
100
120
140
160
180
Seasonal peak of
171M
Unique visitors in May 2016
11. 1111
Why is data science
important to Zillow?
Because Zillow is data
12. 12
Zillow is data
- Our product is driven by data
- The largest most comprehensive housing data (Breadth and depth).
- Over 65 million have been updated by users.
- Our product generates data
- 2MM Reviews of agents.
- More than 300,000 lender reviews.
- 1TB of user activity every day.
- Data is our product
- Users come to Zillow because they trust our housing data.
- Users want to find a trusted agent, and lender that provide great rates and
services.
- We provide data for free for academic/institutional researchers.
- Zillow.com/data – free consumer data (Zillow home value index is available at
a monthly frequency for the nation through states, to neighborhoods.)
13. 13
Data Science and Engineering at Zillow
Clam Bake Beach Day, Aug 2016, at Golden Gardens Park in Seattle, WA
14. 14
Machine Learning at Zillow
Home Valuation
• Zestimate
• Zestimate Forecast
• Zillow Home Value Index
• Rent Zestimate
• Zillow Rent Index
• Pricing Tool
• Best Time to List
B2B
• Ad Campaigns
• Agent segmentation
• Search Engine Marketing (SEM)
Computer Vision
• Videos
• Photos
User Profiles
• Persona Predictions
• Journey location prediction
• Lender Recommendations
Recommendations
• Home recommendation
• Similar homes
• New regions to explore
• Explain recommendations
15. 15
Machine Learning at Zillow
• Example page
Home Valuation
• Zestimate
• Zestimate Forecast
• Rent Zestimate
• Pricing Tool
• Best Time to List
• Zillow Home Value Index
• Zillow Rent Index
example page
16. 16
Zestimate
Goals:
• High Accuracy
• Low Bias
• Independent
• Stable over time.
• Robust to outliers.
• High coverage (Over 100
million homes currently)
• Able to respond to user fact
changes
17. 17
Challenges with the Zestimate
• Some listings are missing features: How do we deal with missing data?
• Some listings have corrupted features (e.g. 28 bathrooms): How do we
identify those?
• Some sale prices do not reflect the value of the home(e.g. a parent
sales to his child): how do we deal with outliers?
• Feature engineering: How can we translate previous sales to
meaningful features?
• How do we identify the places where the model needs to be improve?
18. 18
Machine Learning at Zillow
Home Valuation
• Zestimate
• Zestimate Forecast
• Zillow Home Value Index
• Rent Zestimate
• Zillow Rent Index
• Pricing Tool
• Best Time to List
Computer Vision
• Videos
• Photos
19. 19
Computer Vision at Zillow
• Images and videos play a big role in helping people buy/rent
homes
• Recent deep-learning advancements for CV
20. 20
Let Zillow See
• As of now, our Zestimates are mainly based on
location and size of the properties and they do not
consider the quality.
• Tax assessment might carry house quality
information up to some extent but that’s not
enough.
• For example, an interior upgrade would not change the
tax assessment in most cases if not all
21. 21
• We train a deep convolutional neural network (CNN) to estimate
quality.
Deep Convolutional Neural
Network
Zestimate
23. 23
Machine Learning at Zillow
Home Valuation
• Zestimate
• Zestimate Forecast
• Zillow Home Value Index
• Rent Zestimate
• Zillow Rent Index
• Pricing Tool
• Best Time to List
Computer Vision
• Videos
• Photos
Recommendations
• Home recommendation
• Similar homes
• New regions to explore
• Explain recommendations
25. 25
Home Recommendations
• Our goal is to show users the homes that are relevant to them.
Email
When viewing a home
Ranking search results
26. 26
Email Recommendation
• Goal: Take past user activity and generate relevant recommendations
for new and existing listings.
• Challenges:
• How do we transform user activity into a vector of features?
• What do we want to optimize for? Clicks? Dwell time? Saves?
• What should we do when users don’t have a browsing history (cold start)?
• How can we scale the model to rank 2.5MM homes for 50M buyers? Most
recommendation algorithms are not built for this problem (Netlifx has 5000
movies in its catalog)
31. 31
Machine Learning at Zillow
Home Valuation
• Zestimate
• Zestimate Forecast
• Zillow Home Value Index
• Rent Zestimate
• Zillow Rent Index
• Pricing Tool
• Best Time to List
B2B
• Ad Campaigns
• Agent segmentation
• Search Engine Marketing (SEM)
Computer Vision
• Videos
• Photos
User Profiles
• Persona Predictions
• Journey location prediction
• Lender Recommendations
Recommendations
• Home recommendation
• Similar homes
• New regions to explore
• Explain recommendations
32. 32
Tools
• Spark (Scala and Python)
• R
• Python (numpy, scipy, sklearn, pandas)
• Random forest
• Linear, logistic, quantile regressions.
• Deep neural nets.
• Matrix Factorization
• Etc.
• AWS
33. 33
Zillow Core Values
• Own it.
• Turn on the Lights.
• ZG is a Team Sport.
• Move Fast. Think Big.
• Winning is Fun.
• Act With Integrity
34. 3434
We’re hiring!
• Data Scientist, Computer Vision and Deep learning
• Software Engineer, Machine Learning
• Data Scientist, Machine Learning
• Internship opportunities across Analytics
- Glassdoor reviews: Top 10 in Seattle Business Magazine
100 Best Companies (#3)
- Glassdoor’s Employees’ Choice Best Places to Work;
Glassdoor’s Best Benefits and Perks;
www.zillow.com/jobs
www.zillow.com/data-science
Editor's Notes
Roadmap for today:
Overview of company, data, and culture
Introduce the Data Science and Engineering team and the problems we try to solve
Leave time at the end for general Q&A
Zillow was founded ten years ago with a simple but incredibly ambitious mission: To build the world’s largest, most trusted and most vibrant home-related marketplace.
What this means is that we’re a company which creates a marketplace, and a marketplace has consumers and practitioners., We’re not a brokerage, not an agent, not an MLS; We are creating a marketplace – a place where consumers and producers congregate to conduct commerce with one another.
For buyers:
- We help buyers understand the state of the marketplace, what can they afford
provide them information about each and every listing
recommend homes for them, and alert them when a new relevant listing came to market
Help them to price a listing.
Help them to chose an agent based on rating and number of sales.
For sellers:
Help them to price their home.
See how many people view it online.
Connect them to an agent to help them sell, or let them sell by themselves.
For agents, lenders:
- provide a way to connect with new clients, and to demonstrate their success.
A few years ago Zillow went into rentals and today it’s the leading site in this category in the US.
Here on the bottom right we can see where agents have an opportunity to connect with buyers.
Ten years ago, we were just Zillow, but our brand portfolio grew over time and reflects our mission.
Each brand is striving to empower the consumers through transparency.
Zillow, Trulia and Hotpads focuses on homes and rentals nation wide. StreetEasy and Naked Apartments focus on NYC.
Business brands: Mortgage quotes/rates (Mortech), transaction platform (dotloop)
Huge user base.
30MM rental shopper per month.
First in real estate class - double from our largest competitor (Realtor.com )
78% Market share of all mobile exclusive visitors to real estate category.
In July - Half a billion homes were viewed on Zillow Mobile (270/second) (?????)
Mortgages – 35 million requests in last year
Steven
There are 21 people in the picture. We are actually 48 people now, and have 12 open positions.
Our mission: We attack Zillow’s DS challenges.
Today I’ll talk about the
Start with demo
Zestimate is what made Zillow so famous. It started on day 1, and it what differentiates us from our competitors.
<go over list>
Zillow Home Value index is a economic index derived from the Zestimate. Today it is used by large financial institution, organization and municipalities to understand the real estate market and help decision making. This means that Zestimate is not only helping individuals to value homes, it also help decision makers to understand the housing market.
This is a supervised learning problem. Each home in our dataset, has a set of features associated with it and its sale price. Our goal is to predict the sale price using the features.
David
Netflix page is very personalized and tailored to the user interests.
Each row gives a different way to organize movies.
The first and created by the same model, which gets a collection of movies with a single attributes and rank them according to the user viewing habits.
The second row is from a completely different model the rank similarity between movies.
All these rows are ordered by a third model.
- We would like to simplify the home buying experience and make it as easy a choosing a movie on Netflix.
Each type of recommendation answers different needs.
Email – We would like to send users alerts when their dream home comes on the market, or show them homes that they might wouldn’t consider. The challenge is how not to spam.
When viewing a home, showing other similar homes that the user might like.
When ranking search results, we need to chose the most relevant homes to go to the top of the list.
In recommendation what we usually have is a set of user-item pairs and a corresponding label. The idea is that if we can predict whether a user would like a listing we could make good recommendations.
This seems is a supervised learning problem.
In real life it’s much more complicated.
- How do we know if a user like an item? Most users don’t explicitly tells us. For example, most users don’t rate movies and like videos on youtube.
Even when user tells us, it does not necessary means what we want it to mean. For instance, a user might not like a listing, but it was very relevant for him because at this stage she’s just exploring the market and she would like to understand what she can afford. So listings for homes we will never buy help us understand our options.
The challenge with recommendation is that we never solve for the problem that we would like to solve. We only solve for a surrogate problem. So part of our work is to find the best surrogate problem to solve.
We have a very large catalog.
No of users is on the same order as the number of Items.
No popular items.
Block diagonal matrix
To complicate things, we have features associated with the listings. And we have user activity. How can we translate that to features that are predictive of the outcome.
Shown mission/brands/data – how do we get there
Zillow culture - people
Share people you like
David – ZG is a team sport, turn on the lights (anonymous questions, wikis, open discussion)
Steven –Winning Is Fun – competition, Move Fast Think Big (hackweeks)