The talk at Twente University on 28 July 2014
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Predictive Web Analytics is aimed at understanding behavioural patterns of users of various web-based applications: e-commerce, ubiquitous and mobile computing, and computational advertising. Within these applications business decisions often rely on two types of predictions: an overall or particular user segment demand predictions and individualised recommendations for visitors. Visitor behaviour is inherently sensitive to the context, which can be de ned as a collection of external factors. Context-awareness allows integrating external explanatory information into the learning process and adapting user behaviour accordingly. The importance of context-awareness has been recognised by researchers and practitioners in many disciplines, including recommendation systems, information retrieval, personalization, data mining, and marketing. We focus on studying ways of context discovery and its integration into predictive analytics.
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The talk at Twente University on 28 July 2014
- 1. Context Mining and Integration into Web Predictive Analytics Julia Kiseleva
- 2. Outline • What is Web Predictive Analytics • Context-Aware Predictive Analytics framework • Datasets • User Intent Modeling • Contextual Markov Models • Discovering Change in User intent • Conclusions and Further and Ongoing work 8/21/2014 2
- 4. Let’s give it a try… 8/21/2014 4
- 5. User Intent Modeling: What? • Next action prediction o Click prediction on display advertising o Drop out prediction o User Trail prediction • Information need prediction o Navigational vs. explorative vs. purchase o Changes in user intent • Type of product wanted o Personalization based on context o Personalization based on changed context 8/21/2014 5
- 6. User Intent Modeling: Why? • To understand users and website usage o redesign website o diversified search o search recommendations • To better use advertisement budget o When serve ads? o What type of ads to serve? o brand awareness CPM or convergence CPC • To manipulate user – worth giving a promotion? o personalize with intent of converging to a desired action o personalized suggestions based on user context 8/21/2014 6
- 7. Web Predictive 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 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 8/21/2014 7
- 8. User Intent Modeling: How? Model L Users web log Historical data labels label? 1. training 3. application X y X' labels Testing data 2. testing Training: y = L (X) Application: use L for an unseen data y' = L (X') Formulations: ① Classification ② Regression ③ Clustering ④ Scoring 8/21/2014 8
- 9. Type of Context • User Context o User Preferences o User profiles o Usage of user history • Document/Product Context o Meta-data o Content features • Task Context o Current activity o Location and etc. • Social Context o Leveraging the social graph 8/21/2014 9
- 10. Example of 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 8/21/2014 10
- 11. Example of Context: in Marketing P(Purchase|gender=“male”)=P(Purchase|gender=“female”) ModelMale~f(relevance); ModelFemale~f(perceived value); Gender context Male Female buy relevance buydon’t don’t No context buy relevance don’t gender 8/21/2014 11
- 12. 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 8/21/2014 12
- 13. Taxonomy for explicit Context Human Factors Physical Environment Factors User Characteristics Social Environment Intent Conditions Infrastructure Location *Weather *Light *Acceleration *Audio *… *Temperature *Humidity *… 8/21/2014 13
- 14. Strategies for Context Discovery Definitions/pr operties/utiliti es [Un] [Semi] Supervised methods How to define context Context mining: how to discover context • 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 8/21/2014 14
- 15. Predictive model(s) Predictions Training data Context Integration Output correction if (context == “spring”) select instances(“spring”) if (context == “spring”) select models (“spring”) if (context == “spring”) score += 0.1*score Instance set selection Feature set selection Feature set expansion Model selection/weighting Model adjustment 8/21/2014 15 Strategies for Context Integration
- 18. 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 8/21/2014 18
- 19. Research Questions 1. How to define the context (form and maintain contextual categories) in web analytics? 2. How to connect context with the prediction process in predictive web analytics? 3. How to integrate change detection mechanisms into the prediction process in web analytics? 4. How to ensure integration and feedback mechanisms between change detection and context awareness mechanisms? 5. What should a reference architecture allowing to plug in new context aware prediction techniques for a collection of web analytics tasks look like? 8/21/2014 19
- 20. 8/21/2014 • Context-aware ranking of search results • Drop-out prediction/prev ention • Next action prediction 20
- 21. Mastersportal.eu - Homepage Quick Search Banner Click Universities in the spotlight 8/21/2014 21
- 22. Mastersportal.eu - Search Refine Search Click on Program is Search Result Click on University Click on Country 8/21/2014 22
- 23. Dataset Date Source of information May 2012 Mastersportal.eu #sessions 350.618 #requests 1.775.711 8/21/2014 23
- 24. User Trail Prediction Search Refine Search PaymentClick Product View Click ? What is next? • Does exist any contextual information? • How we can discover it? • How we can utilize it? 8/21/2014 24
- 25. 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 Pay ment 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 Pay ment Session 5 Product View Click on Banner Search 8/21/2014 25
- 26. 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) 8/21/2014 26
- 27. Implicit Context Discover clusters in the graph using communit y detection algorithm c1 = Novice users c1 = Experienced users C = user type 8/21/2014 27
- 28. 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? 8/21/2014 28
- 29. Global vs. explicit vs. implicit vs. random contexts 8/21/2014 29
- 30. Temporal Context- Awareness M1 M2 Mk G H Temporal Context-Awareness: (G,H,ti) …….. t2 t1 tl-1 t3 tl C2 C1 Ck G G H H a1 a2 a3 al-1 al WebSessionS Contextual features Contextual Categories C2 Individual Models 8/21/2014 30
- 32. a b c d abababababcdcdababcdcdcd The number of true predictions = 0 a b c d 1.0 1.0 1.0 1.0 M1 M2 M3 M4 Hierarchical clustering
- 33. a b c d ab abababababcdcdababcdcdcd The number of true predictions = 12 a b c d 1.0 1.0 1.0 1.0 M1 M2 M3 Hierarchical clustering
- 34. a b c d ab abababababcdcdababcdcdcd The number of true predictions = 20 a b c d 1.0 1.0 1.0 1.0 cd M1 M2 Hierarchical clustering
- 35. a b c d ab abababababcdcdababcdcdcd The number of true predictions = 20 a b c d 1.0 1.0 1.0 1.0 cd abcd Stop as long as no additional prediction benefit of merging M1 M2 Hierarchical clustering
- 36. Mastersportal.eu - Search Refine Search Click on Program is Search Result Click on University Click on Country 8/21/2014 36
- 37. Schema for Hieratical Clustering Web log Train: To train predictive models Validation: To find Best clusters Test: To derive final accuracy To find a “Best” clusters Calculate final accuracy To train local predictive models 8/21/2014 37
- 38. Resulted Clusters Id Summary Cluster 1 Intensive Search Basic Search, Refine Search, Empty Search Result 2 Explore information related to program Program impression in search result, Banner click, Program click , Click on university link 3 Start of browsing University Spotlight impression, Quick search 4 Explore country information File view, Click on country link 5 Explore search result Program impressions in search results, University impression on nearby universities 6 Explore program Program in landing page, Submit inquiry 7 Outlier Submit question, X-node 8/21/2014 38
- 40. Site Map • Page is represented as set of possible actions o Example: Homepage is (Quick Search, University Spot light impression, Question Submit) o Calculate Jaccard similarity between Page and Cluster 8/21/2014 40
- 41. Site map Pages Clusters Homepage SearchResult ProgramPage CountryPage UniversityPage Inquiry Submission Question Submission Intensive Search Explore information related to program Start Browsing Explore country information Explore search result Explore program Outlier 8/21/2014 41
- 42. Conclusion • We formulated the context discovery process as an optimization problem • Definition of the useful contexts • Our approach can be used to identify useful contexts for next action prediction • Experiments on a real dataset provide empirical evidence that our methods are better than baselines 8/21/2014 42
- 43. Future works • Testing our method on another datasets • Introducing a mechanism for detecting context switching within a web-session • Considering multidimensional contextual features 8/21/2014 43
- 44. Thank you! • Context mining and integration into prediction models • Accurately predicting users’ desired actions and understanding behavioral patterns of users in various web- applications • Personalization and adaptation to diverse customer needs and preferences • Accounting for the practical needs within the considered application areas 8/21/2014 44
Editor's Notes
- Many taxonomies were built for explictit context. Physical Environment Factors -> Conditions -> Weather -> Context can have different grannularity
- Linking discovered context into predictive modeling or Context-aware adaptation of predictive modeling
- Masterportal homepge to make you familiar with the portal
- Screenshot
- What is the next action prediction of the user? The most common – make use of Markov Model. The question what kind of context we can define and/or discover for this problem
- In order to build a local Markov models we can partition our data. There are two types of partitioning: horizontal and vertical. Let’s start from horizontal. As context we use “user geo location”.
- Jaccard similarity between clusters and pages Explore Intensive search on search result page
- Statement 1 line ----- Meeting Notes (7/28/14 15:14) ----- as an optimization no reading - talk
- ----- Meeting Notes (7/28/14 15:14) ----- Other datasets Another dataset Alighment problem
- Put capa project laog and goal
- Statement 1 line