Challenges and Opportunities in Data Mining: Big...

Center for Web IntelligenceCenter for Web IntelligenceSchool of Computing, DePaul University

Chicago, Illinois, USA

Challenges and Opportunities in Data Mining:Big Data Predictive User Modeling andBig Data, Predictive User Modeling, and

Personalization

Bamshad MobasherCenter for Web IntelligenceCenter for Web Intelligence

School of ComputingDePaul University, Chicago, Illinois, USA

April 20, 2012

Google Trends: Data Mining vs. AnalyticsGoogle Trends: Data Mining vs. Analytics



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The Big Question?The Big Question?

Will data mining remain relevant? If so, how?

Quick survey: Do you think the amount of data available in the digital worldg

will decrease in the future?will become less complex?

Where is the Life we have lost in living?Where is the wisdom we have lost in knowledge?Where is the knowledge we have lost in information?

-- T.S. Eliot, “The Rock”



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,

How much data?Google: ~20-30 PB a dayWayback Machine has ~4 PB + 100-200 TB/month

f /Facebook: ~3 PB of user data + 25 TB/dayeBay: ~7 PB of user data + 50 TB/dayCERN’s Large Hydron Collider generates 15 PB a yearCERN s Large Hydron Collider generates 15 PB a yearIn 2010, enterprises stored 7 Exabytes = 7,000,000,000 GB

640K ought to be enough for anybody.



The Data Tsunami

McKinsy Global Institute Report:“Big Data: the next frontier forg

innovation, competition and productivity”



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Big Data Valueg

McKinsy Global Institute Report:“Big Data: the next frontier for innovation,

competition and productivity”



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What’s Seen the Most Growth in 2008-2011

Types of Data Types of Activities/Areas• Location / Geo / Mobile Data • Search / Web content mining• Music / Audio• Social Media / Social Networks• Time Series

g• Text mining / opinion analysis• Personalization / recommendation• Social network / Social media

• Images / Video• User Profile data• Text feeds / Micro-blog data

analysis• Topic modeling / micro-blog analysis

H lth i f ti

Much of this growth is driven by end user mobile or Web-based applications

• Health informatics

applicationsusers are inundated with huge volume of complex informationneed for more personalized intelligent applications



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Personalization

The ProblemDynamically serve customized content (pages productsDynamically serve customized content (pages, products, recommendations, etc.) to users based on their profiles, preferences, or expected interests

Why we need it?Information spaces are becoming much more complex for userInformation spaces are becoming much more complex for user to navigate (huge online repositories, social networks, mobile applications, blogs, ….)For businesses: need to grow customer loyalty / increase salesFor businesses: need to grow customer loyalty / increase salesIndustry Research: successful online retailers are generating as much as 35% of their business from recommendations



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Data Mining and PersonalizationData Mining and Personalization

“Killer App” for data mining?Tangible successes both in the research and in industrial applications

recommender systemsrecommender systemspersonalized Web agentsuser adaptive systemsWeb marketing and eCRMpersonalized search

Sophisticated modeling approaches based on bothSophisticated modeling approaches based on both predictive and unsupervised DM techniques



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PersonalizationCommon Approaches

Collaborative FilteringCollaborative FilteringGive recommendations to a user based on preferences of “similar” users

Content Based FilteringContent-Based FilteringGive recommendations to a user based on items with “similar” content in the user’s profile

R l B d (K l d B d) Filt iRule-Based (Knowledge-Based) FilteringProvide recommendations to users based on predefined (or learned) rulesage(x, 25-35) and income(x, 70-100K) and childred(x, >=3) recommend(x, Minivan)

Combined or Hybrid Approaches



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The Recommendation Task

Basic formulation as a prediction problem

Given a profile Pu for a user u, and a target item it, predict the preference score of user u on item it

Typically the profile P contains preference scores by u

predict the preference score of user u on item it

Typically, the profile Pu contains preference scores by uon some other items, {i1, …, ik} different from it

preference scores on i1, …, ik may have been obtained explicitly ( i ti ) i li itl ( ti t d t(e.g., movie ratings) or implicitly (e.g., time spent on a product page or a news article)



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The Recommendation Task

Content-Based RecommendationPredictions for unseen (target) items are computed based onPredictions for unseen (target) items are computed based on their similarity (in terms of content) to items in the user profile

C ll b ti R d tiCollaborative RecommendationPredictions for unseen (target) items are computed based the other users’ with similar interest scores on items in user u’sprofile

i.e. users with similar tastes (aka “nearest neighbors”)requires computing correlations between user u and other users

di i iaccording to interest scores or ratingsk-nearest-neighbor (knn) strategy



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Content-Based Recommender

Systems



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Content-Based Recommenders: Personalized SearchPersonalized Search

How can the search i d t i thengine determine the

“user’s intent”??

Query: “Madonna and Child”

??

Need to “learn” the user profile:pUser is an art historian?

User is a pop music fan?



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Content-Based Recommenders:: more examples

Music recommendationsPlay list generation

Example: PandoraCenter for Web IntelligenceCenter for Web Intelligence

School of Computing, DePaul UniversityChicago, Illinois, USA

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Example: Pandora

Collaborative Recommender

Systems



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Collaborative Recommender Systems



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Collaborative Recommender Systems



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Personalization Based on User Behavior Data: Data Mining ApproachData Mining Approach

Typically an Offline ProcessData Preparation / Modeling Phase Pattern Discovery Phase

Implicit or explicit User preference data

(clicktrhoughs, ratings, purchases, reviews

Pattern FilteringAggregation

Pattern Analysis

p ,

Data CleaningData Integration

Data Preprocessing

AggregationCharacterization

AggregateUser Models

Data IntegrationData Transformation

Event Model GenerationSessionization

Data Mining

PatternsContent

& Structure

UserTransaction /PreferenceDatabase

User SegmentationItem Clustering / SimilarityUser/Item Classification

Correlation AnalysisAssociation Rule Mining

Sequential Pattern Mining

Domain Knowledge



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Sequential Pattern Mining

Personalization Based on User Behavior Data: Data Mining ApproachData Mining Approach

Online Process

Recommendation Engine

Recommendations,Integrated

AggregateUser Models

<user,item1,item2, Recommendations,Predictions

gUser Profile

user,item1,item2,…>

Stored User Profile

Web Server Client ApplicationActive SessionDomain Knowledge



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New Challenges g

Context-AwarenessCan s stems nderstand ser’s conte t sit ationCan systems understand user’s context, situation, current intentions?Need to understand “task” being performed; user’s g p ;environment, domain knowledge/characteristics; short-term and long-term preferences

I t ti D i K l dIntegrating Domain KnowledgeMost current modeling approaches focus on the discovery of “shallow” patternsdiscovery of shallow patternsDM + Domain Knowledge (DM + AI) intelligent apps that can reason about / explain patterns



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New Challenges Security / Trust / Reputation

Many user adaptive systems vulnerable to malicious manipulation (e g “shilling”)manipulation (e.g., shilling ) Need more robust algorithms and ways to detect malicious profilesI i l t th ti f “ t ti ” b iti lIn social systems the notion of “reputation” beocmes critical

SerendipityMost predictive models not necessarily the bestp yNeed the ability to “surprise” or provide novelty

Big Data ChallengesQ i f l i f k d l i hQuestions of scale require new frameworks and algorithmsWide variation in user behaviors require more sophisticated models (e.g., matrix factorization, hybrid / ensemble models)



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Challenges:: Problems of Scaleg



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New Opportunities:: Social Annotation S stemsSystems



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Amazon Example: Tags describe the gResource

• Tags can describeTags can describe• The resource (genre, actors, etc)

• Organizational (toRead)• Subjective (awesome)

• Ownership (abc)etc



• etc

Tag RecommendationTag Recommendation



Example: Tags describe the userThese systems are “collaborative.”

Example: Tags describe the user

Recommendation / Analytics based on the “wisdom of crowds.”

Rai Aren's profileRai Aren s profileco-author

“Secret of the Sands"



New Opportunities:: Social RecommendationRecommendation

A form of collaborative filtering using social network data

U filUsers profiles represented as sets of links to other nodes (users or items) in the networkPrediction problem: inferPrediction problem: infer a currently non-existent link in the network



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ConclusionsPersonalization and Recommendation Technologies

The killer app for predictive data analyticsThe killer app for predictive data analyticsWill drive the next generation of Web applications

Lots of new (and old) challengesNew: Social media and social networks provide new challenges and opportunities; big data challenges scalability and effectiveness of old algorithmsscalability and effectiveness of old algorithmsOld: scalability, sparsity, scrutability, serendipityPromising new work:Promising new work:

New approaches to hybridizationSocial media analytics



Context-aware recommendation / personalization31

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