At mobile.de, Germany’s biggest car marketplace, a dedicated data team, supported by the IT project house inovex, is responsible for creating smart data products. One focus are personalised vehicle recommendations to improve the customer experience during browsing as well as finding the perfect offering.
As an introduction, we briefly mention the traditional approaches for recommendation engines, thereby motivating the need for more sophisticated approaches. We then illustrate how Deep Learning can be leveraged to capture the underlying non-linear correlations of features for personalised recommendations. In particular, we’ve customised Google Play’s algorithm for an online marketplace with a fast-changing inventory. Several variants of our adapted approach are evaluated against traditional methods as well as scalability aspects are addressed.
We conclude our talk by giving an outlook on the importance of personalised user experiences and the application of Deep Learning and AI at mobile.de.
Deep Learning-based Recommendations for Germany's Biggest Vehicle MarketplaceFlorian Wilhelm
As presented at the Düsseldorf Data Science Meetup on March, 12th, the talk covers business as well as technical aspects of recommender systems based on deep learning. It is an extended version of the talk held at Bitkom A.I. Summit 2018 with the same title and covers more technical details in depth.
How mobile.de brings Data Science to Production for a Personalized Web Experi...Florian Wilhelm
As Germany's biggest online car marketplace, mobile.de provides a personalized web experience. Our Data Team leverages the interactions of our users to infer their preferences. For this tasks we often apply Python and Spark to wrangle massive amounts of data. In this talk, we are going to present our personalization use-cases as well as the application of PySpark in production.
Bridging the Gap: from Data Science to ProductionFlorian Wilhelm
A recent but quite common observation in industry is that although there is an overall high adoption of data science, many companies struggle to get it into production. Huge teams of well-payed data scientists often present one fancy model after the other to their managers but their proof of concepts never manifest into something business relevant. The frustration grows on both sides, managers and data scientists.
In my talk I elaborate on the many reasons why data science to production is such a hard nut to crack. I start with a taxonomy of data use cases in order to easier assess technical requirements. Based thereon, my focus lies on overcoming the two-language-problem which is Python/R loved by data scientists vs. the enterprise-established Java/Scala. From my project experiences I present three different solutions, namely 1) migrating to a single language, 2) reimplementation and 3) usage of a framework. The advantages and disadvantages of each approach is presented and general advices based on the introduced taxonomy is given.
Additionally, my talk also addresses organisational as well as problems in quality assurance and deployment. Best practices and further references are presented on a high-level in order to cover all facets of data science to production.
With my talk I hope to convey the message that breakdowns on the road from data science to production are rather the rule than the exception, so you are not alone. At the end of my talk, you will have a better understanding of why your team and you are struggling and what to do about it.
As Germany’s largest online vehicle marketplace mobile.de uses recommendations at scale to help users find the perfect car. We elaborate on collaborative & content-based filtering as well as a hybrid approach addressing the problem of a fast-changing inventory. We then dive into the technical implementation of the recommendation engine, outlining the various challenges faced and experiences made.
Which car fits my life? Mobile.de’s approach to recommendationsinovex GmbH
Description
As Germany’s largest online vehicle marketplace mobile.de uses recommendations at scale to help users find the perfect car. We elaborate on collaborative & content-based filtering as well as a hybrid approach addressing the problem of a fast-changing inventory. We then dive into the technical implementation of the recommendation engine, outlining the various challenges faced and experiences made.
Abstract
At mobile.de, Germany’s biggest car marketplace, a dedicated team of data engineers and scientists, supported by the IT project house inovex is responsible for creating intelligent data products. Driven by our company slogan “Find the car that fits your life”, we focus on personalised recommendations to address several user needs. Thereby we improve customer experience during browsing as well as finding the perfect offering. In an introduction to recommendation systems, we briefly mention the traditional approaches for recommendation engines, thereby motivating the need for sophisticated approaches. In particular, we explain the different concepts including collaborative and content-based filtering as well as hybrid approaches and general matrix factorisation methods. This is followed by a deep dive into the implementation and architecture at mobile.de that comprises ElasticSearch, Cassandra and Mahout. We explain how Python and Java is used simultaneously to create and serve recommendations.
By presenting our car-model recommender that suggests similar car models of different brands as a concrete use-case, we reiterate on key-aspects during modelling and implementation. In particular, we present a matrix factorisation library that we used and share our experiences with it. We conclude by a brief demonstration of our results and discuss the improvements we achieved in terms of key performance indicators. Furthermore, we use our use case to exemplify the usage of deep learning for recommendations, comparing it with other traditional approaches and hence providing a brief account of the future of recommendation engines.
Event: PyData Berlin 2017
Speaker: Dr. Florian Wilhelm (inovex), Dr. Arnab Dutta (mobile.de)
Mehr Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/
Tech-Blog: https://www.inovex.de/blog/
Arolys | The key of your competitiveness / Value Analysis and Design to CostArolys
http://www.arolys.com
Professional of competitiveness, we bring companies to recast their product offers for competitive savings thanks to simple, straightforward and fast methods: design to cost and value analysis.
http://www.arolys.com/index.php/en/Conseil-competitivite/Design-to-cost/product-competitiveness-counselling.html
http://www.arolys.com/index.php/en/Conseil-competitivite/Design-to-cost/competitive-design.html
http://www.arolys.com/index.php/en/Conseil-competitivite/Conseil-innovation/product-innovation.html
http://www.arolys.com/index.php/en/Conseil-competitivite/Reduction-diversite/rationalized-diversity.html
Deep Learning-based Recommendations for Germany's Biggest Vehicle MarketplaceFlorian Wilhelm
As presented at the Düsseldorf Data Science Meetup on March, 12th, the talk covers business as well as technical aspects of recommender systems based on deep learning. It is an extended version of the talk held at Bitkom A.I. Summit 2018 with the same title and covers more technical details in depth.
How mobile.de brings Data Science to Production for a Personalized Web Experi...Florian Wilhelm
As Germany's biggest online car marketplace, mobile.de provides a personalized web experience. Our Data Team leverages the interactions of our users to infer their preferences. For this tasks we often apply Python and Spark to wrangle massive amounts of data. In this talk, we are going to present our personalization use-cases as well as the application of PySpark in production.
Bridging the Gap: from Data Science to ProductionFlorian Wilhelm
A recent but quite common observation in industry is that although there is an overall high adoption of data science, many companies struggle to get it into production. Huge teams of well-payed data scientists often present one fancy model after the other to their managers but their proof of concepts never manifest into something business relevant. The frustration grows on both sides, managers and data scientists.
In my talk I elaborate on the many reasons why data science to production is such a hard nut to crack. I start with a taxonomy of data use cases in order to easier assess technical requirements. Based thereon, my focus lies on overcoming the two-language-problem which is Python/R loved by data scientists vs. the enterprise-established Java/Scala. From my project experiences I present three different solutions, namely 1) migrating to a single language, 2) reimplementation and 3) usage of a framework. The advantages and disadvantages of each approach is presented and general advices based on the introduced taxonomy is given.
Additionally, my talk also addresses organisational as well as problems in quality assurance and deployment. Best practices and further references are presented on a high-level in order to cover all facets of data science to production.
With my talk I hope to convey the message that breakdowns on the road from data science to production are rather the rule than the exception, so you are not alone. At the end of my talk, you will have a better understanding of why your team and you are struggling and what to do about it.
As Germany’s largest online vehicle marketplace mobile.de uses recommendations at scale to help users find the perfect car. We elaborate on collaborative & content-based filtering as well as a hybrid approach addressing the problem of a fast-changing inventory. We then dive into the technical implementation of the recommendation engine, outlining the various challenges faced and experiences made.
Which car fits my life? Mobile.de’s approach to recommendationsinovex GmbH
Description
As Germany’s largest online vehicle marketplace mobile.de uses recommendations at scale to help users find the perfect car. We elaborate on collaborative & content-based filtering as well as a hybrid approach addressing the problem of a fast-changing inventory. We then dive into the technical implementation of the recommendation engine, outlining the various challenges faced and experiences made.
Abstract
At mobile.de, Germany’s biggest car marketplace, a dedicated team of data engineers and scientists, supported by the IT project house inovex is responsible for creating intelligent data products. Driven by our company slogan “Find the car that fits your life”, we focus on personalised recommendations to address several user needs. Thereby we improve customer experience during browsing as well as finding the perfect offering. In an introduction to recommendation systems, we briefly mention the traditional approaches for recommendation engines, thereby motivating the need for sophisticated approaches. In particular, we explain the different concepts including collaborative and content-based filtering as well as hybrid approaches and general matrix factorisation methods. This is followed by a deep dive into the implementation and architecture at mobile.de that comprises ElasticSearch, Cassandra and Mahout. We explain how Python and Java is used simultaneously to create and serve recommendations.
By presenting our car-model recommender that suggests similar car models of different brands as a concrete use-case, we reiterate on key-aspects during modelling and implementation. In particular, we present a matrix factorisation library that we used and share our experiences with it. We conclude by a brief demonstration of our results and discuss the improvements we achieved in terms of key performance indicators. Furthermore, we use our use case to exemplify the usage of deep learning for recommendations, comparing it with other traditional approaches and hence providing a brief account of the future of recommendation engines.
Event: PyData Berlin 2017
Speaker: Dr. Florian Wilhelm (inovex), Dr. Arnab Dutta (mobile.de)
Mehr Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/
Tech-Blog: https://www.inovex.de/blog/
Arolys | The key of your competitiveness / Value Analysis and Design to CostArolys
http://www.arolys.com
Professional of competitiveness, we bring companies to recast their product offers for competitive savings thanks to simple, straightforward and fast methods: design to cost and value analysis.
http://www.arolys.com/index.php/en/Conseil-competitivite/Design-to-cost/product-competitiveness-counselling.html
http://www.arolys.com/index.php/en/Conseil-competitivite/Design-to-cost/competitive-design.html
http://www.arolys.com/index.php/en/Conseil-competitivite/Conseil-innovation/product-innovation.html
http://www.arolys.com/index.php/en/Conseil-competitivite/Reduction-diversite/rationalized-diversity.html
This presentation by Hal Varian, Professor of Berkeley School of Information, was made during the discussion on "Big Data: Bringing competition policy to the digital era" held during the 126th meeting of the OECD Competition Committee on 29 November 2016. More papers and presentations on the topic can be found out at www.oecd.org/daf/competition/big-data-bringing-competition-policy-to-the-digital-era.htm
The event is a knowledge exchange platform bringing together all stakeholders playing an active role in the innovation, technology, connected car & autonomous driving business development scene.
Europe’s leading event in this field is a combination of inspirational expert keynotes and well-moderated, interactive World Café sessions, private discussion rounds and networking sessions. Join the conversations of over 2 days of knowledge exchange to gain a deeper understanding of the latest trends, disruptors in the future of connected car and autonomous vehicle ecosystem & impact on automotive smart mobility business strategies.
This presentation contains an elaborate (Porter's) Five-Forces Analysis of Car2Go in Frankfurt am Main as a "Free-floating Car-sharing" provider. Additionally, you can find a detailed S-W-O-T Analysis and followinh strategic recommendations for the defined market in Frankfurt, Germany. It has been a strategy project for university so all used information and content is publicly available.
What's Next: The Next Frontier in Automotive Industry Ogilvy Consulting
Between homes and offices most of us spend large amounts of time commuting but a growing awareness of transportation’s ecological impact has triggered a shift towards public and shared transport. Automobiles continue to play a significant role in society, but one that is changing. The entire automotive industry is witnessing massive disruption throughout the value chain.
Digital technology in particular is proving the lever that is shifting the gears of transformation and driving ever more innovative customer experiences. In this session we will understand the major trends driving the automotive industry and how creating and being a part of a pivotal ecosystem experience, is critical for shared success.
Building a recommender system with Annoy and Word2Vec by Cristian PEREZ, Kern...recsysfr
The Kernix Lab will talk about the development of a recommender engine at the RecSys MeetUp. We will discuss both strategic and technical considerations for a production ready system. Technically, how we handle cold start, misspelled words and content high renewal rates will be shared.
Digital Transformation in Automotive Industry Chinese-German CAR Symposiumaccenture
China has now the world’s largest netizen population and the largest e-commerce market. With a large population of “always on” consumers, the digital eco-system is evolving rapidly in China and is drastically redefining customer experience management in all key dimensions. Fast development of connected car technology and its new applications have created a new biz platform. What does this mean for car manufacturers?
Omniconvert is a growth marketing platform for conversion rate optimization.
It empowers eCommerce, lead generation, and SaaS marketers do A/B testing,
web personalization, and surveys.
Kia turns website visitors into showroom visitors with a mobile app, user pro...IgnitionOne
Niko Nelisson from Tap Crowd presents how Kia turned website visitors into showroom visitors with a mobile app at IgnitionOne's European Automotive Summit, June 2014
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During the recent XD Atlanta meeting: "Customer Experience in the Rise of the Digital Age" — I kicked off a leadership panel with this presentation focused on shifts in customer behavior as more products and services turn to digital.
GreenRoad presentation in the future of IoT, connected car and Shared Mobility. Driver Safety and Fleet Management are part of the future of Connected car, Shared Mobility and IoT.
State of IoT review. beyond predictive maintenance and asset management. Value based IoT solutions. Data driven and digital transformation. IoT platform
Cloud based simulation
High end Edge computing
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Streamlining Python Development: A Guide to a Modern Project SetupFlorian Wilhelm
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The talk was given at PyConDE / PyData Berlin 2024. More details here: https://pretalx.com/pyconde-pydata-2024/talk/CBVTEG/
In this presentation, you will be introduced to the concept of Integer Programming and its application in conference scheduling. We will delve into the fundamentals of Integer Programming and its practical utilization in optimizing the allocation of talks to specific time slots and rooms within a conference program. By the conclusion of the talk, attendees will gain a clearer comprehension of the potential of this powerful tool in creating a conference schedule that is both efficient and effective, ultimately maximizing attendee satisfaction. Whether you are involved in conference organization or simply curious about optimization algorithms, this presentation is tailored to meet your interests.
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This presentation by Hal Varian, Professor of Berkeley School of Information, was made during the discussion on "Big Data: Bringing competition policy to the digital era" held during the 126th meeting of the OECD Competition Committee on 29 November 2016. More papers and presentations on the topic can be found out at www.oecd.org/daf/competition/big-data-bringing-competition-policy-to-the-digital-era.htm
The event is a knowledge exchange platform bringing together all stakeholders playing an active role in the innovation, technology, connected car & autonomous driving business development scene.
Europe’s leading event in this field is a combination of inspirational expert keynotes and well-moderated, interactive World Café sessions, private discussion rounds and networking sessions. Join the conversations of over 2 days of knowledge exchange to gain a deeper understanding of the latest trends, disruptors in the future of connected car and autonomous vehicle ecosystem & impact on automotive smart mobility business strategies.
This presentation contains an elaborate (Porter's) Five-Forces Analysis of Car2Go in Frankfurt am Main as a "Free-floating Car-sharing" provider. Additionally, you can find a detailed S-W-O-T Analysis and followinh strategic recommendations for the defined market in Frankfurt, Germany. It has been a strategy project for university so all used information and content is publicly available.
What's Next: The Next Frontier in Automotive Industry Ogilvy Consulting
Between homes and offices most of us spend large amounts of time commuting but a growing awareness of transportation’s ecological impact has triggered a shift towards public and shared transport. Automobiles continue to play a significant role in society, but one that is changing. The entire automotive industry is witnessing massive disruption throughout the value chain.
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China has now the world’s largest netizen population and the largest e-commerce market. With a large population of “always on” consumers, the digital eco-system is evolving rapidly in China and is drastically redefining customer experience management in all key dimensions. Fast development of connected car technology and its new applications have created a new biz platform. What does this mean for car manufacturers?
Omniconvert is a growth marketing platform for conversion rate optimization.
It empowers eCommerce, lead generation, and SaaS marketers do A/B testing,
web personalization, and surveys.
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Niko Nelisson from Tap Crowd presents how Kia turned website visitors into showroom visitors with a mobile app at IgnitionOne's European Automotive Summit, June 2014
Customer Experience in the Rise of the Digital Age — Atlanta XD Meeting 9/13/...Jeremy Johnson
During the recent XD Atlanta meeting: "Customer Experience in the Rise of the Digital Age" — I kicked off a leadership panel with this presentation focused on shifts in customer behavior as more products and services turn to digital.
GreenRoad presentation in the future of IoT, connected car and Shared Mobility. Driver Safety and Fleet Management are part of the future of Connected car, Shared Mobility and IoT.
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Session Overview
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https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
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The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...
Deep Learning-based Recommendations for Germany's Biggest Online Vehicle Marketplace
1. Deep Learning-based Recommendations for
Germany’s Biggest Online Vehicle Marketplace
Bigdata.AI Summit, Hanau, March 1, 2018
Florian Wilhelm, Arnab Dutta
2. 2
Introduction
Marcel Kurovski
Data Scientist
inovex GmbH
@FlorianWilhelm
FlorianWilhelm
florianwilhelm.info
Dr. Arnab Dutta
Data Scientist
mobile.de GmbH
@kopfhohen
kraktos
o
Dr. Florian Wilhelm
Data Scientist
inovex GmbH
squall-1002
5. 5
IT-project house for digital transformation:
‣ Agile Development & Management
‣ Web · UI/UX · Replatforming · Microservices
‣ Mobile · Apps · Smart Devices · Robotics
‣ Big Data & Business Intelligence Platforms
‣ Data Science · Data Products · Search · Deep Learning
‣ Data Center Automation · DevOps · Cloud · Hosting
‣ Trainings & Coachings
Using technology to inspire our
clients. And ourselves.
inovex offices in
Karlsruhe · Pforzheim · Köln
München · Hamburg · Stuttgart.
www.inovex.de
11. 11
Recommendations on View Item Page
VIP
Recommendations based on the
specific make and model a user is
viewing to present alternatives
WishlistHome SRP View Contact Buy
12. 12
Recommendations on your Wishlist
Recommendations based on the
specific make and model of a
deleted ad to provide almost
identical recommendations
Recommendations based on the
users car preferences and the
parking lot items.
WishlistHome SRP View Contact Buy
15. 15
Summary of Collaborative Filtering
üCollective behaviour of users
üStandard-Method (it works, it’s reliable etc.)
x Cold Start Problem: New listings need a
certain number of clicks to be recommended.
x Sparsity problems: lot fewer interaction
data points than total items and users.
x Content agnostic
x Only “batch-based” learning
16. 16
Looking For: Used Car (100%)
Prefers (Make): BMW (50%), Audi (50%)
Prefers (Model): Audi A3 (25%), Audi A4 (25%),
BMW 318 (50%)
Searching In: lat 52.5206, lon 13.409
Search Radius: 300km
Preferred Price: 20 000€ ± 1500€
Preferred Mileage: 10 000km ± 5000km
User Preferences
Anonymous
Content-based Filtering: User Preferences
18. 18
Summary of Content-based
üWorks even if there are no other
users
ücontent-based preferences of
users based on a weighted vector
of item features
xHard to do recommendations for
new users (cold start problem)
xNon-applicable for heterogenous
content types
xLow diversity, i.e. more of the same
19. 19
Traditional Hybrid Recommender
Collaborative
Filtering
Hybrid
Recommender
Content
based
PP
P P
P
Looking For: Used Car (100%)
Prefers (Make): BMW (50%), Audi (50%)
Prefers (Model): Audi A3 (25%), Audi A4 (25%),
BMW 318 (50%)
Searching In: lat 52.5206, lon 13.409
Search Radius: 300km
Preferred Price: 20 000€ ± 1500€
Preferred Mileage: 10 000km ± 5000km
User Profile
Buyer
Last Action: Yesterday
Frequent User
User 12345
Likelihood to buy: 88 %
Elastic Search Query
ü based on ES and Mahout
ü comprehensible and debuggable
ü robust and reliable concepts
ü easy to tune for different use-cases
x incapable of capturing inherent non-
linear feature dependencies
x lots of manual feature engineering
21. 21
Deep Learning
„[...] reported a 29%
sales increase to
$12.83 billion [...]“
Deep Learning Success StoriesReasons for Deep Learning
• captures nonlinear relations
• holistic approach
• less feature engineering
• improved quality
Search
Recommendations
22. 22
Find the car that perfectly fits your life
User’s Car Preferences Car Pool + Attributes
(make, model, color, price, …)
Flexible
(cold-start, uncertainty, real-time, ...)
Interactions of other users
(views, parkings, contacts)
25. 25
Deep Learning Recommender - Architecture
ad storage
embeddings
RankNet
UserNet
ItemNet
Candidate
Generation
ANN Index
Candidate ServiceRanking Service
Web Service
User Preference API
Recommendation Service
26. 26
Technology Stack
Annoy ANN by
Spotify
Hardware
GPU-Server
NVIDIA Tesla K80
4x Intel Xeon 3.5 GHz
64GB RAM,
850GB Disk
LightFM
by Lyst
28. 28
Improvements by Deep Learning
0,25%
0,35%
0,45%
0,55%
0,65%
0,75%
0,85%
0,95%
1,05%
1,15%
k = 1 k = 5 k = 10 k = 30 k = 100
MAP@k
Collaborative Filtering
Traditional Hybrid
Deep Recommender
+73%
+143%
