putting the person into personalization by elizabeth churchill
DESCRIPTION
IT as a Utility Network+ community conference 19-20 June 2014, Southampton (ITaaU Network+)TRANSCRIPT
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Pu#ng the Person into Personaliza1on
Elizabeth F. Churchill eBay Research Labs
June 2014
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U=lity
noun: u=lity -‐ the state of being useful, profitable, or beneficial -‐ a public u=lity
adjec=ve: u=lity
-‐ useful, able to perform several func=ons -‐ func=onal rather than aJrac=ve
Infrastructural
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Changing models of commerce
• Consump=on • Distribu=on • Produc=on
• “Personaliza=on” ….. different perspec=ves – Marke=ng, content & product targe=ng – Engineering as customisa=on – Design as co-‐produc=on
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Some context
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ψ
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Human Computer Interac=on
a discipline concerned with the design, implementa3on and evalua3on of
interac3ve compu3ng systems for human use and with the
study of major phenomena surrounding them
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Algorithmic Living*(s)
Behaviour system – rela=ons of the data
Source System
Data system – interfaces are conduit from source to data
“Algorithmic Living” is a phrase borrowed from Paul Dourish (UCI) and colleagues
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Social Sciences Design Sciences
Computa=onal Sciences
Design paEerns
Informa3on (re)representa3on Informa3on architecture, interac3on design, graphic design
Cross device interac3ons
Field research methods
Prototyping
Human Factors
Experience design
Brain & Body Sciences Percep3on, emo3on
Cogni3ve Psychology Decision making (interrup3on and), memory, language, emo3on, individual differences
Social psychology Social iden3ty, trust, communica3on
Anthropology Shopping & the circula3on of goods
Machine Learning Personaliza3on/Recommenda3on
Informa3on architecture(s) Catalogues, user representations
Search Sciences
Data Sciences, Social Network Analysis & Computa3onal sociology
Sociology Demographics, SNA
Web services Graph theory
Collabora3ve Filtering Collabora3ve Filtering
Data mining
Design recommenda3ons & requirements
Data Data representa3on
Adap3ve interfaces & interac3ions
System requirements
HCI & Product/Service Design
Computer Graphics Opera3ng Systems
Linguis3cs
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Design aware data, data aware design • The interface brokers a conversa=on between a user and a service, it
invites (“affords”) ac=ons • Proac=ve data collec=on versus reac=ve data analy=cs
– Too much focus on the numbers and not on the insights – Ar=factual collec=ons versus inten=onal programma=c collec=on – Assume
• Tangible and intangible signals • Weak signals • Par=al footprints, composite persons (shared devices/accounts), bots • Misleading informa=on, missing pieces, incomplete stories
• Ques=ons to ask yourself – What are you asking to know about a person? Why?
• What is the design ra=onale for your instrumenta=on? – Are the data you are gathering fit for your purpose, valid, reliable?
• Are your conclusions jus=fied?
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Data shaping, data design
People - ”users”
Teams
Organisational
Societal/Regulatory
Interfaces Micro
Macro & Meta
Business Meso
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People - ”users”
Teams
Organisational
Societal/Regulatory
Interfaces Micro
Macro & Meta
Business Meso
Experience mining For “deep understanding” rather than “deep learning”, we need to triangulate methods: experiments, ethnography, interviews
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Triangulate data Triangulate perspec3ves Explore different units of analysis Eyetracking Lab experiments Interviews Prototypes (lo and hi) Focus groups Surveys Ethnographic field studies Experimental field studies Ac=vity log (”trace”) analysis Data mining Data visualiza=on Computer scien=sts Anthropologists Sociologists Vision Scien=sts Designers: product, interac=on, graphic
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Personaliza1on
Increasing Relevance of Content and Presenta=on Modality/Style
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Recommender Systems – the abstracted & “generic” consumer
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Concierge personaliza=on: “the ‘n’ of 1”
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Outcome Personaliza=on: Search
Non-‐personalized Personalized
F(f)ossil collector
E-‐commerce Product Search: Personaliza3on, Diversifica3on, and Beyond, A=sh Das Sarma, Nish Parikh, Neel Sundaresan, Tutorial at WWW-‐2014
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outcome what
(e.g., content match/relevance)
process how, where, when
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Kinect Myo
Google glass Emo=v, brain & eye control
Process
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Consump1on
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Auc=onweb …the history of eBay
hJp://mashable.com/2010/08/07/ebay-‐facts/
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hJp://mashable.com/2010/08/07/ebay-‐facts/
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Value is beyond the ar=fact The broken laser pointer…the history of eBay
hJp://mashable.com/2010/08/07/ebay-‐facts/
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hJp://mashable.com/2010/08/07/ebay-‐facts/
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hJp://mashable.com/2010/08/07/ebay-‐facts/
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Literacy, reputa=on & trust
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Selling exper=se…
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Simplifying selling
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Commerce 3.0 for Development: The promise of the Global Empowerment Network
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Distribu1on
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Commerce 3.0 for Development: The promise of the Global Empowerment Network
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Commerce 3.0 for Development: The promise of the Global Empowerment Network
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Modern Spice Routes: The Cultural Impact of Cross-‐Border Shopping
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Modern Spice Routes: The Cultural Impact of Cross-‐Border Shopping
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Modern Spice Routes: The Cultural Impact of Cross-‐Border Shopping
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Commerce 3.0 for Development: The promise of the Global Empowerment Network
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Digital consump1on is s1ll about material produc1on & distribu1on, global s1ll means local delivery or pick-‐up
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Digital consump1on is s1ll about material produc1on & distribu1on, global s1ll means local delivery or pick-‐up
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Digital consump1on is s1ll about material produc1on & distribu1on, global s1ll means local delivery or pick-‐up
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Modern Spice Routes: The Cultural Impact of Cross-‐Border Shopping
Under the percentages are people and their prac1ces….
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Modern Spice Routes: The Cultural Impact of Cross-‐Border Shopping
Under the percentages are people and their purchases….
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Produc1on
Changing modes of
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hJp://www.inc.com/magazine/201306/eric-‐markowitz/how-‐to-‐choose-‐a-‐crowdfunder.html
Crowdfunding
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Crowdfunding and “par1cipatory” produc1on
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Made to order – “possibility” shop fronts
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Printed to order – “possibility” shop fronts
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Printed to order – “possibility” shop fronts
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hJp://www.betabrand.com/ Par1cipatory fashion
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Par1cipatory fashion
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Algorithmic Living(s)
Behaviour system – rela=ons of the data
Source System
Data system – interfaces are conduit from source to data
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Changing models of commerce
• Consump=on • Distribu=on • Produc=on
• “Personaliza=on” ….. different perspec=ves – Marke=ng, content & product targe=ng – Engineering as customisa=on – Design as co-‐produc=on
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Commerce 3.0 for Development: The promise of the Global Empowerment Network
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(David) Ayman Shamma
M. Cameron Jones
Elizabeth F. Churchill
Ques=ons &/or Comments? [email protected]
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References • Pu\ng the person back into personaliza3on. interac=ons 20.5 (2013): 12-‐15,
Elizabeth Churchill • From data divina3on to data-‐aware design. interac=ons 19.5 (2012): 10-‐13,
Elizabeth Churchill • E-‐commerce Personaliza3on at Scale 23rd Interna=onal Conference on
Informa=on and Knowledge Management (CIKM), Elizabeth Churchill, A=sh Das Sarma, Ranjan Sinha
• Data Design for Personaliza3on: Current Challenges and Emerging Opportuni3es, Elizabeth Churchill, A=sh Das Sarma, Workshop at WSDM-‐2014
• E-‐commerce Product Search: Personaliza3on, Diversifica3on, and Beyond, A=sh Das Sarma, Nish Parikh, Neel Sundaresan, Tutorial at WWW-‐2014
• Commerce 3.0 for Development: The promise of the Global Empowerment Network hJp://www.ebaymainstreet.com/news-‐events/commerce-‐30-‐development-‐promise-‐global-‐empowerment-‐network
• Modern Spice Routes: The Cultural Impact of Cross-‐Border Shopping hJps://www.paypal-‐media.com/assets/pdf/fact_sheet/PayPal_ModernSpiceRoutes_Report_Final.pdf