The document discusses the role of recommender systems in predictive analytics, emphasizing their importance in forecasting and targeting within commercial settings. It categorizes different types of recommendation algorithms, such as content-based and collaborative filtering, and explores their applications in e-commerce and decision support scenarios. Additionally, it highlights the integration of statistical methods with recommender systems to enhance prediction capabilities.

1. b
b
© Know-Center GmbH, www.know-center.at
Die Rolle von Empfehlungssystemen
in Predictive Analytics
VERSTEHEN & KATEGORISIEREN VON REC-SYSTEMS IN DER PRAXIS
Sebastian Dennerlein & Florian Geigl
Predictive Analytics – 10.10. @ Tech Gate, Wien

2. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
UNDERSTANDING OF PREDICTION
2
Nicht erschöpfende Methodensammlung und alternative Zugänge
Statistische Methoden zur Vorhersage von
zukünftigen Datenpunkten (Forecasting):
» Lineare Verfahren: Regressionsanalysen
» Nicht lineare Verfahren: Machine Learning
ABER alternative Zugänge wie
Empfehlungssysteme oder
Recommender Systems!

3. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
When forecasting methods are deployed in
commercial usage, the field is known
as predictive analytics.
Siegel, Eric (2013). Predictive analysis: The power to predict who will click, buy, lie, or die. John
Wiley & Sons, Hoboken, NJ, 302 p.
KC VISION FOR DATA-DRIVEN MARKETING
INCLUDES STRONG PREDICTION FOCUS:
‘marketing without data is like
driving with your eyes closed!’
2 foci: forecasting & targeting

4. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics 4
KC‘s Commercial Usage of Prediction Methods

5. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
TARGETING CUSTOMERS WITH
PERSONALIZED & CONTEXTUALIZED OFFERS
5
Definition von Recommender Systems
» In everyday life we rely on recommendations from other people either by
word of mouth, recommendation letters, movie and book reviews printed in
newspapers.
[Ricci et al., 2011]
» Recommender Systems are software agents that elicit the interests and
preferences of individual consumers and make recommendations
accordingly.
[Xiao & Benbasat, 2007]

6. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
TARGETING CUSTOMERS WITH
PERSONALIZED & CONTEXTUALIZED OFFERS
6
Defining Recommender Systems
» Problemstellung:
• Ausgangssituation:
• (User) Model
• einer Person oder
• eines Objektes
• Artefakte
• Ziel:
• Relevante Artefakte gereiht nach
Relevanz
• ODER: Inferenzen über Artefakte
und enthaltene Items
» Voraussetzung: Interaktivität
» Prozess: Algorithmen
[Traubetal.,2015;Lacicetal.,2015b]

7. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
RECOMMENDER SYSTEMS
IN PREDICTIVE ANALYTICS
7
Classifying Recommender Systems for Prediction? Questions
» In welchem Zusammenhang stehen nun klassische statistische Prediction
Verfahren und Recommender Systems?
» Klassifikation von verschiedenen Rec-Systems?
Zusammenhang Algorithmus und Application Scenario:
AC1: E-Commerce
???
AC2: Decision Support
???
AC3: Forecasting
???

8. b
b
© Know-Center GmbH, www.know-center.at
Application Scenario - Misc
E-COMMERCE RECOMMENDER
&

9. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Wie finden Kunden was sie mögen?
9
A B

10. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Wie finden Kunden was sie mögen?
10
Ich mag Hot Dog B viel lieber,
weil er originale Frankfurter
Würstchen verwendet!
A B
Similar Price Similar Price
Content

11. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
A B
Wie finden Kunden was sie mögen?
11
Similar Price Similar Price
Social

12. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Wie finden Kunden was sie mögen?
12
147 rated this item2 rated this item
A B
Similar Price Similar Price
Frequency

13. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Recommendation Algorithmen
13
» Nicht-personalisierte
Recommendations
verwenden statistische Daten
einer Community ohne User
Models in Betracht zu ziehen
» Personalisierte
Recommendations
betrachten das User Model
und kontextuelle Daten für die
Berechnung der Vorschläge

14. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Content-Based Filtering (CBF)
14
» Content-Based Filtering explores the data about
items (characteristics) and users (preferences) to
generate the recommendations
» The system learns to recommend items that are
similar to the ones that the user liked in the past
[Jannach et al., 2010]
Figure . Representation of the Content Based paradigm RS (Recommender Systems: An introduction, 2011)

15. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Collaborative Filtering (CF)
15
» Predicts what a user will like/dislike based on their
similarity to other users
» we need to collect and analyse transactional data
(ratings provided by users implicitly or explicitly).
Figure . Representation of the Content Based paradigm RS (Recommender Systems: An introduction, 2011)

16. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
ENTSPRECHENDE REC-ALGORITHMEN
16

17. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Hybrids
17
» Run different recommendation algorithms
» Combine output to generate more robust and accurate
results
» Store intermediate results (before combining)
Item-1
Item-2
Item-3
Item-1
Item-4
Item-2
Item-1
Item-3
Item-4
Item-2Alg. 1 Alg. 2 Alg. 3

18. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Example – Hotel Booking: Predict Alternative
EXAMPLE
» Hotel Booking Recommender

19. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Example – E-Commerce: Predict Products
EXAMPLE
» E-Commerce - Webshop

20. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Example – Conference Assistant: Predict Talks
EXAMPLE
» Conference Assistant – Finding Similar Peers

21. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Example – AFEL: Predict Topics & Resources
RECOMMENDER SYSTEMS
IN PREDICTIVE ANALYTICS
» Zusammenhang Algorithmus und Application Scenario:
AC1: E-Commerce
Klassische Algos:
Un-Personalisiert
Personalisiert
Content-Based
Collaborative Filtering
AC2: Decision Support
???
AC3: Forecasting
???

22. b
b
© Know-Center GmbH, www.know-center.at
Application Scenario - Detego
DATA-DRIVEN DECISION SUPPORT IN FASHION RETAIL
&

23. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
PROCESSING WORKFLOW
23
Business
Understanding
Statistical
Analysis
Mining &
Modeling
Testing &
Evaluation
Framework
Integration
Data
Acquisition
Data
Understanding
Data
Preparation
Deployment
Real-life
Evaluation
Based on CRISP-DM = Cross Industry Standard Process for Data Mining
DATA PREPARATION
DATA SCIENCE

24. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
PROCESSING WORKFLOW
24
Based on CRISP-DM = Cross Industry Standard Process for Data Mining
Business
Understanding
Statistical
Analysis
Mining &
Modeling
Testing &
Evaluation
Framework
Integration
Deployment
Real-life
Evaluation
Data
Acquisition
Data
Understanding
Data
Preparation

25. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
DETEGO
25
Understanding your customer remains paramount
» Today's “always-on” customers jump between sales
channels on a whim, forcing retailers to meet growing
expectations for “shopping anywhere, anytime”. The
focus is no longer so much on which articles a
retailer wants to sell, but rather to whom they want
to sell as well as their individual wants and needs.
Any friction between offline and online retailing
consequently leads to lost sales. Customer centricity
demands a complete realignment of in-store processes,
technologies and personnel.
» 2 Trends: IOT & Predictive Recommendations

26. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
PROCESSING WORKFLOW
26
Based on CRISP-DM = Cross Industry Standard Process for Data Mining
Business
Understanding
Statistical
Analysis
Mining &
Modeling
Testing &
Evaluation
Framework
Integration
Deployment
Real-life
Evaluation
Data
Acquisition
Data
Understanding
Data
Preparation

27. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
PROJEKTIDEE
27

28. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
PROCESSING WORKFLOW
29
Based on CRISP-DM = Cross Industry Standard Process for Data Mining
Business
Understanding
Statistical
Analysis
Mining &
Modeling
Testing &
Evaluation
Framework
Integration
Deployment
Real-life
Evaluation
Data
Acquisition
Data
Understanding
Data
Preparation

29. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
HUNDERTE LÄDEN
30

30. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
TAUSENDE ARTIKEL
31

31. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
MILLIONEN TEILE
32

32. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
PROJEKTIDEE
33
» Top-Seller empfehlen
» Artikel Ranking
» Geschäfte Leaderboard
» Gamification
» Artikel welche sich in einem Geschäft anormal
“verhalten”
» Outlier Detection
Ziele

33. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Relative Sales
Time to sold
Salesfloor Exposure
OUTLIER METRIKEN
34

34. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
PROCESSING WORKFLOW
35
Business
Understanding
Statistical
Analysis
Mining &
Modeling
Testing &
Evaluation
Framework
Integration
Deployment
Real-life
Evaluation
Data
Acquisition
Data
Understanding
Data
Preparation
Based on CRISP-DM = Cross Industry Standard Process for Data Mining

35. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
ÄHNLICHE LÄDEN
36

36. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
ÄHNLICHE LÄDEN
37

37. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
CLUSTERING
38

38. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
DATA ANALYSIS
39
Ranking
Outlier
# SoldTime to Sold
Salesfloor
Exposure
Kombination Stückzahl
Signifikanz-
test
P-Value
Konfidenz
Metriken

39. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
OUTLIER DETECTION
40
TimetoSoldTimetoSold

40. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
PROCESSING WORKFLOW
41
Business
Understanding
Statistical
Analysis
Mining &
Modeling
Testing &
Evaluation
Framework
Integration
Real-life
Evaluation
Data
Acquisition
Data
Understanding
Data
Preparation
Deployment
Based on CRISP-DM = Cross Industry Standard Process for Data Mining

41. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
INTEGRATION
42

42. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
CONCLUSIO
43
Zusammenfassung und Lessons Learned

43. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Example – AFEL: Predict Topics & Resources
RECOMMENDER SYSTEMS
IN PREDICTIVE ANALYTICS
» Zusammenhang Algorithmus und Application Scenario:
AC1: E-Commerce
Klassische Algos:
Un-Personalisiert
Personalisiert
Content-Based
Collaborative Filtering
AC2: Decision Support
Outlier Detection:
Time to sold
Relative sales
Salesfloor exposure
AC3: Forecasting
???

44. b
b
© Know-Center GmbH, www.know-center.at
Application Scenario - Porsche
DEMAND FORECASTING
&

45. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
PROCESSING WORKFLOW
46
Based on CRISP-DM = Cross Industry Standard Process for Data Mining
Business
Understanding
Statistical
Analysis
Mining &
Modeling
Testing &
Evaluation
Framework
Integration
Deployment
Real-life
Evaluation
Data
Acquisition
Data
Understanding
Data
Preparation

46. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
PHASEN BEIM AUTOKAUF
47
Deloitte Studie 2016 – „Pain Points in der heutigen Customer Journey
beim Automobilkauf“

47. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
MARKTSTRUKTUR
48
Relevante Daten
Neuwagenmarkt
Händler
Serviceersatz-
fahrzeug
Haltedauer: 60-90 Tage
Vorführwagen
Haltedauer: 60-90 Tage
Tages- und
Kurzzulassungen
6 - 60 Tage
Rent a Car Flotten Privatkunden
Gebrauchtwagen-
markt
FOKUS

48. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
PROCESSING WORKFLOW
49
Based on CRISP-DM = Cross Industry Standard Process for Data Mining
Business
Understanding
Statistical
Analysis
Mining &
Modeling
Testing &
Evaluation
Framework
Integration
Deployment
Real-life
Evaluation
Data
Acquisition
Data
Understanding
Data
Preparation

49. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
DATA INGESTION &
PROCESSING
50
» Ausgangspunkte:
» Neu- & Gebrauchtwagenzulassungen
» Abmeldedaten
» Externe Daten (z.B. Werktage)
» Aggregationen
» Marken
» Segmente
» Fabrikate
» Fahrzeugtypen

50. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
PROCESSING WORKFLOW
51
Based on CRISP-DM = Cross Industry Standard Process for Data Mining
Business
Understanding
Statistical
Analysis
Mining &
Modeling
Testing &
Evaluation
Framework
Integration
Deployment
Real-life
Evaluation
Data
Acquisition
Data
Understanding
Data
Preparation

51. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
DATA UNDERSTANDING
52
» Übersicht der Zulassungsdaten einzelner Marken

52. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
DATA UNDERSTANDING
53
» Autokorrelation zeigt für die meisten Modelle ein wiederkehrendes
Verhalten nach 12 Monaten
» Ausprägung der Autokorrelation ist stark modellspezifisch

53. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
PROJEKTIDEE
54
Recommendations and Decision Support via Predicitve Analytics
» Verstehen des Marktes und dessen Entwicklung
basierend auf historischen Zulassungsdaten
» Auswahl von ähnlichen Marken zur Orientierung der
Vorhersagen für bestehende eigene Modelle
(Bestimmung des Marktanteils)

54. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
PROCESSING WORKFLOW
55
Based on CRISP-DM = Cross Industry Standard Process for Data Mining
Business
Understanding
Statistical
Analysis
Mining &
Modeling
Testing &
Evaluation
Framework
Integration
Deployment
Real-life
Evaluation
Data
Acquisition
Data
Understanding
Data
Preparation

55. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
DATA ANALYSIS
56
Vergleich unterschiedlicher Methoden
» Regressions basierte Modelle
» Linear Regression
» Gaussian Processes
» MLP Regressor
» Mulitlayer Perceptron
» SMO Reg
» Neuronale Netzwerke
» Feed Forward Networks
» Recurrent Networks
» Zeitreihen basierte Analyse
» AutoRegressive Integrated Moving Average (ARIMA)
» Seasonal ARIMA

56. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
PROCESSING WORKFLOW
57
Based on CRISP-DM = Cross Industry Standard Process for Data Mining
Business
Understanding
Statistical
Analysis
Mining &
Modeling
Testing &
Evaluation
Framework
Integration
Data
Acquisition
Data
Understanding
Data
Preparation
Deployment
Real-life
Evaluation

57. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
REAL LIFE EVALUATION &
ACTUAL IMPACT
58
» Prediction Ergebnisse über die Zeit

58. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
REAL LIFE EVALUATION &
ACTUAL IMPACT
59
» Fehlerübersicht über die Zeit

59. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics 60
New Car
Registrations
Used Car
Registrations
Sales Data
External Data
e.g. Workdays
Data Processing
extracting & aggregating Information, unifying brand & model codes
Statistical Analysis
Cross-Correlations, Seasonality, ARIMA
Forecast Generation
Linear Model (SARIMA) or Non-Linear Model (LSTM-RNN)
Recommendation & Control Dashboard
Error Metrics, Approach Comparison, Hybrid Combination

60. b
b
© Know-Center GmbH, www.know-center.at
The Role of Recommendations in
Predictive Analytics
Conclusio & Classification

61. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
CONCLUSIO
62
» Recommender als Prediction Approach
» Vervollständigen des Outfits
» Neben Flug Hotel vorschlagen
» …prinzipiell nächste folgende Entscheidung vorhersagen.
» Chance der Verbindung von statistischen Methoden
und Forecasting Verfahren mit Recommendern
 Klassifikation von Recommender Systems!

62. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Example – AFEL: Predict Topics & Resources
RECOMMENDER SYSTEMS
IN PREDICTIVE ANALYTICS
» Rec-Basics (Classical Recommender – E-Commerce) vs
Advanced Statistics (Detego & Porsche)
» Zusammenhang Algorithmus und Application Scenario:
AC1: E-Commerce
Klassische Algos:
Un-Personalisiert
Personalisiert
Content-Based
Collaborative Filtering
AC2: Decision Support
Outlier Detection:
Time to sold
Relative sales
Salesfloor exposure
AC3: Forecasting
Customer Prediction:
Seasonal ARIMA
Recurrent Neural
Newtrorks

63. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
ScaR Framework
Scalable Recommender
DEMO: http://scar.know-center.tugraz.at/demo.html
USPs: Scalable, Real-Time, Context-Sensitive & Keep Own Data in House
[Traubetal.,2015;Lacicetal.,2015b]

64. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Referenzen
65
» Lacic, E., Kowald, D., Eberhard, L., Trattner, C., Parra, D., & Marinho, L. B.
(2015a). Utilizing online social network and location-based data to recommend
products and categories in online marketplaces. In Mining, Modeling, and
Recommending'Things' in Social Media (pp. 96-115). Springer International
Publishing.
» Lacic, E., Traub, M., Kowald, D., & Lex, E. (2015b). ScaR: Towards a Real-Time
Recommender Framework Following the Microservices Architecture.
» Traub, M., Kowald, D., Lacic, E., Schoen, P., Supp, G. & Lex, E. (2015). Smart
booking without looking: providing hotel recommendations in the TripRebel
portal. In Proceedings of the 15th International Conference on Knowledge
Technologies and Data-driven Business (i-KNOW '15). ACM, New York, NY,
USA.
» Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender
systems handbook (pp. 1-35). Springer US.
» Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents:
Use, characteristics, and impact. Mis Quarterly, 31(1), 137-209.

65. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Data-Driven Marketing mit dem Know-Center
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics 66

66. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Know-Center Forschungsbereiche
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics 67

67. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Know-Center Geschäftsfelder
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics 68

68. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Möglichkeiten zur
Zusammenarbeit für maßgeschneiderte Lösungen
im Data-Driven Marketing
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics 69
Gefördert via
SFG / FFG...
oder direkt

69. © Know-Center GmbH
Know-Center GmbH
Research Center for Data-Driven
Business and Big Data Analytics
Inffeldgasse 13/6
8010 Graz, Austria
Firmenbuchgericht Graz
FN 199 685 f
UID: ATU 50367703
gefördert durch das Programm COMET (Competence Centers for Excellent Technologies), wir danken unseren Fördergebern:
Deputy Head & Business Development
@ Know-Center Gmbh
sdennerlein@know-center.at
Mag. Sebastian Dennerlein
Project Manager
@ Know-Center Gmbh
mtraub@know-center.at
Dipl. Ing. Matthias TraubDr. Florian Geigl
Data Scientist
@ Detego GmbH
f.geigl@detego.com
Wir sind Ihre Ansprechpartner für
maßgeschneiderte Lösungen
• aller Arten von Datenanalysen wie
Vorhersagen oder personalisierte Empfehlungssysteme &
• intelligenter Datenvisualisierungen
in Fashion & Retail und anderen Domänen.