Putting the Person into Personalization by Elizabeth Churchill

Pu#ng the Person into Personaliza1on

Elizabeth F. Churchill eBay Research Labs

June 2014

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

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

Some context

ψ

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

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

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

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?

Data shaping, data design

People - ”users”

Teams

Organisational

Societal/Regulatory

Interfaces Micro

Macro & Meta

Business Meso

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

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

Personaliza1on

Increasing Relevance of Content and Presenta=on Modality/Style

Recommender Systems – the abstracted & “generic” consumer

Concierge personaliza=on: “the ‘n’ of 1”

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

outcome what

(e.g., content match/relevance)

process how, where, when

Kinect Myo

Google glass Emo=v, brain & eye control

Process

Consump1on

Auc=onweb …the history of eBay

hJp://mashable.com/2010/08/07/ebay-‐facts/


Value is beyond the ar=fact The broken laser pointer…the history of eBay




Literacy, reputa=on & trust

Selling exper=se…

Simplifying selling

Commerce 3.0 for Development: The promise of the Global Empowerment Network

Distribu1on



Modern Spice Routes: The Cultural Impact of Cross-‐Border Shopping




Digital consump1on is s1ll about material produc1on & distribu1on, global s1ll means local delivery or pick-‐up




Under the percentages are people and their prac1ces….


Under the percentages are people and their purchases….

Produc1on

Changing modes of

hJp://www.inc.com/magazine/201306/eric-‐markowitz/how-‐to-‐choose-‐a-‐crowdfunder.html

Crowdfunding

Crowdfunding and “par1cipatory” produc1on

Made to order – “possibility” shop fronts

Printed to order – “possibility” shop fronts

Printed to order – “possibility” shop fronts

hJp://www.betabrand.com/ Par1cipatory fashion

Par1cipatory fashion

Algorithmic Living(s)

Behaviour system – rela=ons of the data

Source System

Data system – interfaces are conduit from source to data

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


(David) Ayman Shamma

M. Cameron Jones

Elizabeth F. Churchill

Ques=ons &/or Comments? [email protected]

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

Technology

Putting the Person into Personalization by Elizabeth Churchill