Context Mining and Integration in Web Predictive Analytics
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Context Mining and Integration in Web Predictive Analytics
- 1. Context mining and integration into Predictive Web Analytics Julia Kiseleva (Eindhoven University of Technology), Supervised by: Mykola Pechenizkiy (Eindhoven University of Technology),
- 2. Web Predictive Analytics What is predictive web analytics? Web predictive analytics: • aims to predict individual and aggregated characteristics indicating visitor behavior for purposes of understanding and optimizing web usage. • Application: o Search engines o Recommender System • Examples: o Computational Advertisement • Predictive web analytics tasks: o Online shop’s recommendations; o Users’ next action prediction; o Users’ intention predicting; o Personalized search result page.
- 3. Model L Users web log Historical data labels label? 1. training 3. application X y X' y’=L (X') Formulations: ① Classification ② Regression ③ Clustering ④ Scoring labels Testing data 2. testing Predictive Web Analytics
- 4. User next action prediction Historical data. Actions ={Search, Refine Search, Click on Banner, Product view, Payment} Search Refine Search ?Click on Banner Product View What is next? 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 Running Example: users’ trail predictions
- 5. Search Refine Search ?Click on Banner Product View What is next? Running Example: users’ trail predictions Search Refine Search Payment Click on Banner Product View 1.0 2/3 1/3 1/2 1/4 Drop out 3/4 1/4 1 1/4 User next action prediction
- 6. Context What is context? – any additional information that Why we need context? o enhances the understanding of the instance of interest, o helps us to classify this instance or makes predictions regarding its behavior. • Two major context types: o Explicit – stored explicitly or given by domain expert (location, OS, Browser) o Implicit – hidden in the data. We need techniques to discover context.
- 7. Taxonomy for explicit Context Human Factors Physical Environment Factors User Characteristics Social Environment Intent Conditions Infrastructure Location *Weather *Light *Acceleration *Audio *… *Temperature *Humidity *…
- 8. Environment/ Context Model L Users web log X' y' Historical data labels X y label? labels Test data Strategies: ① ? ② ? ③ ? ④ ? Context-Awareness in Web Predictive Analytics
- 9. Research Questions Question 1: How to define the context in predictive web analytics? Question 2: How to connect context with the prediction process in predictive web analytics? Context Definition Context Discovery Context Modeling Context Mining: How define context? Context Integration
- 10. Search Refine Search ?Click on Banner Product View What is next? Running Example: users’ trail predictions Search Refine Search Payment Click on Banner Product View 1.0 2/3 1/3 1/2 1/4 Drop out 3/4 1/4 1 1/4 User next action prediction
- 11. Local models
- 12. Contextual Partitioning • Approaches to create local models: o Horizontal partitions 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
- 13. Contextual Partitioning • Approaches to create local models: o Horizontal partition o Vertical partition : • Two types of behavior: o Ready to by – (Product View, Payment) o Just browsing – (Search, Refine Search, Click on Banner) 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
- 14. Contextual Partitioning • Approaches to create local models: o Horizontal partition o Vertical partition : • Two types of behavior: o Ready to by – (Product View, Payment) o Just browsing – (Search, Refine Search, Click on Banner) 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
- 15. Context Definition • Intuition about Context: change of user intents o User is looking for the product o User is ready to buy Search Refine Search Payment Click on Banner Product View Intent: looking for product Intent: ready to buy
- 16. Context Discovery • Context definition: change of user intents o User is looking for the product o User is ready to buy • Context discovery – apply hierarchical clustering in order to maximize prediction accuracy Search Refine Search PaymentClick Product View Intent: looking for product Intent: ready to buy
- 18. Context Integration Example Context: User intent DATA L1 L2 Contextual Categories Individual Learners Mapping G Mapping H Context Discovery Ready to buy Just browsing
- 19. Context Integration Example …………… … C1 C2 C3 Cn Contextual features Fs DATA Environment L1 L2 L3 Lk Contextual Categories Individual Learners Mapping G Mapping H Context Discovery
- 20. Thank you! • Context identification and integration it into prediction models • Accurately predicting users’ desired actions and understanding behavioral patterns of users in various web-applications • Personalization and adaptation to diverse customer need and preferences • Accounting for the practical needs within the considered application areas.
- 21. Summary • The main goal is to develop a generic framework for context-aware systems for Web Predictive Analytics • In order to archive this goal we need to answer the following questions: o How to define the context in predictive web analytics? o How to connect context with the prediction process in predictive web analytics? Questions?
- 22. Research Methodology Implementing CAPA framework Developing CAPA framework Online validation (A/B testing) Internal validation
- 23. Context-aware systems Context definition Context Integration Method Application Context-aware system Recommendation systems Computational Advertisement Information Retrieval Normalization Expansion Classifier Selection Classification Adjustment Weighting Domain Expert Clustering Contextual feature identification
- 24. History of context definition and discovery Context Year Location 1992 Taxonomy of explicit context 1999 Predictive features vs. contextual 2002 Hidden context: (clustering, mixture models) 2004 Contextual bandits 2007 History of previous interaction 2008 Independence of predicted class 2011 Two level prediction model 2012 Focus on Context Discovery 2012 - Timeline
- 25. Research Goal • Our research aims to develop a generic framework and corresponding techniques for introducing the contextual information in Predictive Web Analytics and accounting for the practical needs within the considered application areas.
Notes de l'éditeur
- Good day! My name is …, I am from Eindhoven university of Technology, My supervisor is MykolaPechenizkiy.I am happy present.
- Let’s define what WPA is. Def on slideApplication of WPA on slide, make example – personalized search result page
- Let’s consider a simple design of web predictive analytics.In WPA we commonly use the following formulation: ON SLIDE to solve WPA tasks.
- Let’s consider concrete example of user’ next action prediction.How we can predict next user’ action?We have user’ web log. The most commonly used model for sequence prediction is Markov Models.
- We can process our historical data and build a user navigation graph. Which is suitable to train a markov models.
- Let’s consider what is a context. ON THE SLIDE
- Many taxonomies were built for explictit context.Physical EnvironmentFactors -> Conditions -> Weather -> Context can have different grannularity
- The question what kind of context could be useful for WPA and how we can utilize it.Our research aims to develop a generic framework and corresponding techniques for introducing the contextual information in Predictive Web Analytics and accounting for the practical needs within the considered application areas.
- Main Resear question on the slideMain focus of my work is to define and discovery useful context and find ways to integrate this context.
- We can process our historical data and build a user navigation graph. Which is suitable to train a markov models.We can incorporate context to build local markov models which is easy to train and parametrized.
- Let’s consider the following exampleExplain how it can be helpful
- In order to build a local Markov models we can partiotion our dat.There are two types of partitioning: horizontal and vertical. Let’s start from horizontal. As context we use “user geo location”.
- Vertical partition based on user intent
- We have defined a context as “user intent”, which help us to make a vertical partition.
- How we can discovery user intent?
- My focus is building local models.Contextual information Input adjustment, model selection, output adjustment
- Let’s consider our running example for model selection.
- More general schema
- Addчто сделано
- Лучше убрать
- Add examples instead of historyRunning examplesAdd definition of explicit and implicit contexts
- Our research aims to develop a generic framework and corresponding techniques for introducing the contextual information in Predictive Web Analytics and accounting for the practical needs within the considered application areas