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Rolle Empfehlungssysteme in Predictive Analyticsb

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Der Vortrag „The Role of Recommendations in Predictive Analytics“ geht zunächst auf Data-Driven Marketing, dessen Grundpfeiler und Recommender Systems als Teil von Marketing Services im Bereich Predictive Analytics ein. Im Anschluss wird dies anhand von Projekten veranschaulicht, und unter anderem eine in Kooperation mit Detego entwickelte, Decision Support Engine vorgestellt, welche basierend auf der Analyse von RFID Event-Streams konkrete Handlungsempfehlungen für MitarbeitInnen der Retail Fashion Industrie ableitet und damit deren digitale Transformation auf ein neues Level hebt.

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Rolle Empfehlungssysteme in Predictive Analyticsb

  1. 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. 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. 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. 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. 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. 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. 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. 8. b b © Know-Center GmbH, www.know-center.at Application Scenario - Misc E-COMMERCE RECOMMENDER &
  9. 9. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Wie finden Kunden was sie mögen? 9 A B
  10. 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. 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. 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. 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. 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. 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. 16. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics ENTSPRECHENDE REC-ALGORITHMEN 16
  17. 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. 18. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Example – Hotel Booking: Predict Alternative EXAMPLE » Hotel Booking Recommender
  19. 19. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Example – E-Commerce: Predict Products EXAMPLE » E-Commerce - Webshop
  20. 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. 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. 22. b b © Know-Center GmbH, www.know-center.at Application Scenario - Detego DATA-DRIVEN DECISION SUPPORT IN FASHION RETAIL &
  23. 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. 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. 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. 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. 27. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics PROJEKTIDEE 27
  28. 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. 29. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics HUNDERTE LÄDEN 30
  30. 30. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics TAUSENDE ARTIKEL 31
  31. 31. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics MILLIONEN TEILE 32
  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. 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. 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. 35. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics ÄHNLICHE LÄDEN 36
  36. 36. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics ÄHNLICHE LÄDEN 37
  37. 37. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics CLUSTERING 38
  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. 39. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics OUTLIER DETECTION 40 TimetoSoldTimetoSold
  40. 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. 41. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics INTEGRATION 42
  42. 42. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics CONCLUSIO 43 Zusammenfassung und Lessons Learned
  43. 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. 44. b b © Know-Center GmbH, www.know-center.at Application Scenario - Porsche DEMAND FORECASTING &
  45. 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. 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. 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. 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. 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. 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. 51. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics DATA UNDERSTANDING 52 » Übersicht der Zulassungsdaten einzelner Marken
  52. 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. 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. 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. 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. 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. 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. 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. 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. 60. b b © Know-Center GmbH, www.know-center.at The Role of Recommendations in Predictive Analytics Conclusio & Classification
  61. 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. 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. 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. 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. 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. 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. 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. 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. 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.

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