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Predicting Current User Intent with Contextual Markov Models

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Abstract—In many web information systems like e-shops and information portals predictive modeling is used to understand user intentions based on their browsing behavior. User behavior is inherently sensitive to various contexts. Identifying such relevant contexts can help to improve the prediction performance. In this work, we propose a formal approach in which the context
discovery process is defined as an optimization problem. For simplicity we assume a concrete yet generic scenario in which context is considered to be a secondary label of an instance that is either known from the available contextual attribute (e.g. user location) or can be induced from the training data (e.g. novice vs. expert user). In an ideal case, the objective function of the optimization problem has an analytical form enabling us
to design a context discovery algorithm solving the optimization problem directly. An example with Markov models, a typical approach for modeling user browsing behavior, shows that the derived analytical form of the optimization problem provides us with useful mathematical insights of the problem. Experiments with a real-world use-case show that we can discover useful contexts allowing us to significantly improve the prediction of
user intentions with contextual Markov models.

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Predicting Current User Intent with Contextual Markov Models

  1. 1. Predicting Current User Intent with Contextual Markov Models Julia Kiseleva, Hoang Thanh Lam, Mykola Pechenizkiy (TU/e) Toon Calders (ULB) DDDM@ICDM2013, Dallas, TX, USA CAPA project: http://www.win.tue.nl/~mpechen/projects/capa/ 7 December 2013
  2. 2. Outline • What is predictive Web analytics • Context-Aware Predictive Analytics framework • User intent modeling • Contextual Markov Models • Case study, experimental results • Conclusions and further ongoing work DDDM@ICDM2013 Dec 7, 2013 1Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  3. 3. Understanding user needs DDDM@ICDM2013 Dec 7, 2013 2Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  4. 4. Let’s give it a try… DDDM@ICDM2013 Dec 7, 2013 3Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  5. 5. User Intent Modeling: What? • Next action prediction – Click prediction in display advertising – Drop out prediction – Trail prediction • Information need prediction: – Navigational vs. explorative vs. purchase – Open acronym based on context • Type of product wanted – Personalization based on context DDDM@ICDM2013 Dec 7, 2013 4Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  6. 6. User Intent Modeling: Why? • To understand users and website usage – redesign website, redirect flows, – diversified search, recommendations • To better use budget (pageviews) – what (type of) ads to serve? – brand awareness CPM, or convergence CPC • To manipulate user – worth giving a promotion? – personalize with intent of converging to a desired action – personalized suggestions based on user context DDDM@ICDM2013 Dec 7, 2013 5Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  7. 7. User Intent Modeling: How? Model L population (source) Historical data labels label? 1. training 2. 2. application X y X' y' Training: y = L (X) Application: use L for an unseen data y' = L (X') labels Testing data DDDM@ICDM2013 Dec 7, 2013 6Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  8. 8. Context in IR & RecSys • User Context – Preferences, usage history, profiles • Document/Product Context – Meta-data, content features • Task Context – Current activity, location etc. • Social Context – Leveraging the social graph DDDM@ICDM2013 Dec 7, 2013 7Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  9. 9. Context in Diagnostics Not predictive alone but a subset of features with the contextual attribute(s) becomes (much) more predictive Time of the day context no context DDDM@ICDM2013 Dec 7, 2013 8Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  10. 10. Context in Marketing P(Purchase|gender=“male”)=P(Purchase|gender=“female”) ModelMale~f(relevance); ModelFemale~f(perceived value) gender context no context Male Female buy buy relevance relevance buy don’t don’t don’t gender DDDM@ICDM2013 Dec 7, 2013 9Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  11. 11. Environment/ Context Model L population Training: ?? Application: y' = Lj (X') Lj <= G(X',E) X' y' Historical data labels X y label? Context-Awareness as Meta-learning labels Test data DDDM@ICDM2013 Dec 7, 2013 10Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  12. 12. Learning Classifiers & Context DDDM@ICDM2013 Dec 7, 2013 11Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  13. 13. Research Questions • How to define the context (form and maintain contextual categories) in web analytics? • How to connect context with the prediction process in predictive web analytics? • How to integrate change detection mechanisms into the prediction process in web analytics? • How to ensure integration and feedback mechanisms between change detection and context awareness mechanisms? • What should a reference architecture allowing to plug in new context aware prediction techniques for a collection of web analytics tasks look like? DDDM@ICDM2013 Dec 7, 2013 13Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  14. 14. IEEE CBMS 2010 Perth, Australia Handling Concept Drift in Medical Applications: Importance, Challenges and Solutions © M. Pechenizkiy and I. Zliobaite 14 • Context-aware ranking of search results • Drop-out prediction/pre vention • Next action prediction
  15. 15. Mastersportal.eu - Homepage Quick Search Banner Click Universities in the spotlight
  16. 16. Mastersportal.eu - Search Refine Search Click on Program is Search Result Click on University Click on Country
  17. 17. User Navigation Graph
  18. 18. Motivation for Contextual Markov Models Useful Contexts: E[M] < pc1*E[Mc1] + pc2*E[Mc2] Why should it help? Explicit contexts (user location) Implicit contexts (inferred from clickstream)
  19. 19. Implicit Context Discover clusters in the graph using community detection algorithm c1 = Novice users c1 = Experienced users C = user type DDDM@ICDM2013 Dec 7, 2013 19Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  20. 20. Dataset Date Source of information May 2012 Mastersportal.eu #sessions 350.618 #requests 1.775.711 DDDM@ICDM2013 Dec 7, 2013 20Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology Publicly available at: http://www.win.tue.nl/~mpechen/projects/capa
  21. 21. Accuracy Results DDDM@ICDM2013 Dec 7, 2013 21Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology user location user type
  22. 22. Global vs. explicit vs. implicit vs. random contexts DDDM@ICDM2013 Dec 7, 2013 22Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  23. 23. Conclusions • We formulated context discovery as optimization problem • Our approach can be used to identify useful contexts • Experiments on a real dataset provide empirical evidence that contextual Markov Models are more accurate than global models • Further (ongoing) work – Temporal context discovery (TempWeb@WWW’2013) – Multidimensional vertical and horizontal clustering on the user navigation graph DDDM@ICDM2013 Dec 7, 2013 23Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  24. 24. Change of Intent as Context Switch Timeline t5t0 t3t2 t4 t1 Search Refine Search PaymentClick Product View Search Click t6 Context ``Find information” Context ``Buy product” What is next? Change of intent? DDDM@ICDM2013 Dec 7, 2013 24Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  25. 25. User next action prediction Search Refine Search PaymentClick Product View Click ? • What the context is attached to? o Single action? o Session/trail? (user) o A group of sessions (space/time) • Pattern-mining based approach Collaboration is welcome! DDDM@ICDM2013 Dec 7, 2013 25Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  26. 26. Designing Context-awareness Predictive model(s) PredictionsTraining data Context-aware Adaptation Instance set selection Feature set selection Feature set expansion Model selection/weighting Model adjustment Output correction if (context == “spring”) select instances(“spring”) if (context == “spring”) select models (“spring”) if (context == “spring”) score += 0.1*score DDDM@ICDM2013 Dec 7, 2013 26Predicting Current User Intent with Contextual Markov Models Mykola Pechenizkiy, Eindhoven University of Technology
  27. 27. Designing Context-awareness Definitions/ properties/ utilities [Un] [Semi]Super vised methods How to define context Context mining: how to discover context Instance set selection Feature set selection Feature set expansion Model selection/weighting Model adjustment Output correction Contextual features Contextual categories Features not predictive alone, but increasing predictive power of other features Descriptors explaining a significant group of instances having some distinct behaviour Subgroup discovery AntiLDA Uplift modeling Actionable attributes
  28. 28. Horizontal Partitioning Users from Europe Users from South America Session 1 Search Refine Search Click on Banner Product View Payment Session 3 Product View Payment Session 3 Search Refine Search Refine Search Click on Banner Session 4 Search Refine Search Click on Banner Product View Payment Session 5 Product View Click on Banner Search
  29. 29. Horizontal Partitioning
  30. 30. Two types of behavior: Ready to buy – (Product View, Payment) Just browsing – (Search, Refine Search, Click on Banner) Session 1 Search Refine Search Click on Banner Product View Payment Session 2 Product View Payment Session 3 Search Refine Search Refine Search Click on Banner Session 4 Search Refine Search Click on Banner Product View Payment Session 5 Product View Click on Banner Search Vertical Partitioning
  31. 31. Session 1 Search Refine Search Click on Banner Product View Payment Session 2 Product View Payment Session 3 Search Refine Search Refine Search Click on Banner Session 4 Search Refine Search Click on Banner Product View Payment Session 5 Product View Click on Banner Search Two types of behavior: Ready to buy – (Product View, Payment) Just browsing – (Search, Refine Search, Click on Banner) Vertical Partitioning

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