Personalisation, user models, privacy and filter bubbles

Judy Kay&

Bob Kummerfeld

Overview

• User models– Static (HCI old)– Dynamic (HCI+AI)

• Individual• Group

– Personas, collaborative-filter models

• Personalisation– recommenders

• Privacy, personal data• User control and interfaces challenges• Filter Bubbles

User models

These are becoming increasingly important as personalisation

becomes very common

User model

• A set of beliefs about the user• Static user model in HCI is

– A set of belief about “the user” ie all users– in the programmer’[s] head[s]– implicit: often not written down, embedded in code– influence the design of the interface – are embedded within the code

• Dynamic user models (HCI + AI + recommenders)– explicit different sets of beliefs

• about a single user • or a group of user

– explicitly represented, separately from the code

User model• A set of beliefs about the user• Static user model in HCI is

– A set of beliefs a programmer may have about “the user” – May include invalid assumptions– Or partially valid ones, that are true of some users– May never be explicitly recorded– Examples to consider for correctness:

• Prefer large fonts– Compromises amount of text

• Prefer interfaces with lots of colour – Poor colour blind users, if the colour matters and they cannot see it as needed

• Prefer saturated colours– At odds with recommendations for colour use

• Prefer icons over text in English– Often relies on user’s mental model for recognition of the icon– But English text relies on user’s knowledge of English

• Know to “mouse over” the screen to get explanations– Depends on user’s mental model

• Expect keyboard shortcuts as alternative to menus– Depends on user’s mental model

Many applications are personalised

• Important examples include– Recommenders

• eg Netflix, Amazon

– Teaching systems • eg Cognitive Tutors

– Many commercial sites • eg for selection of ads

– Search engines • eg to alter ranking taking location into account

• They have a dynamic user model (“user model” == “dynamic user model” for the rest of the lecture)

Building user models

• How to learn about the user?– Ask them .. explicit modelling– Observe their behaviour .. implicit modelling

• Recall lecture on design of questionnaires and the nature of the questions people can answer reliably

Case study• Online dating• User volunteers information .. an explicit user model

– about themselves (user model of the user’s self-beliefs)– About the people they would like recommended (user model of preferences)

• Interface and recommender designer– Aims to match explicit model– Capture details of user activity

• who they appear to like• And who appears to like them (will reciprocate)

• Class questions to consider: what are the issues for the system and interface designer?– Are explicit user models more reliable? – Or implicit?– Is it ok to ignore the explicit model if the implicit one is more effective?– Does the user want to know why a person was recommended to them?– Are there other challenges to consider?

PersonalisationCustomised versus Adaptive interfaces

• User defines the parameters that control the interface– Set font size

• user model: prefer larger fonts

– Print double sided • user model: prefer to always print double-sided

• Class activity in groups of 2-3:– Identify 5 examples of customisation in web browsers– Identify problems in achieving desired customisation

Customisation

Customisation

• Frequently not easy for user to find how to control the customisation

• Many users are afraid to customise as they fear that will not be able to alter settings if they later want to– Good interfaces may allow reset to defaults– Better would be for users to find and review the history

• Can be considered similar to a simple form of user model

• possibly have a file of customisation settings • That could be imported by multiple applications

– For this to be effective, need agreed standard for elements and their meaning (“ontology”)

Recommenders• Amazon … many recommenders

– eg People who liked X also liked {y1, y2 … y3}• Individual’s dynamic user model

– eg books bought [not as gifts] … inference that user likes these books• Personalisation approaches

– Content-based• eg user who has bought “sci-fi” books has a user model component indicating they

like sci-fi and system recommends books with metadata tag “sci-fi”

– Collaborative filters• Create “huge 2-d grid” (actually sparse, and so optimised)

– Each row is one user u(i)– Each column in one book b(j)– Each cell indicates whether user i bought book j

• Algorithm matches this user to “similar” users– Cosine, clustering … many algorithms

Example: Amazon

• if you have an Amazon account and login (ie identify yourself to Amazon) it will recommend new items to you

• explanations are provided for the recommendations

• you are allowed to change the user model

Stereotypes• Many software systems ask people to answer a single question and then

drive personalisation (usually with very few options)– EG: Please rate your familiarity with this system:

• Beginner• Advanced

• Identify a small set of useful clusters (stereotypes)• Need to then determine which stereotype applies to the current user• Personalise based on the stereotype• Double stereotypes

– eg person who has used sophisticated aspects of an application is an expert (so observing user activity, even for a short time, means system can infer they are an expert … then many assumptions about the user’s knowledge

– A person who says they are an expert in an application knows many sophisticated aspects (so asking the person, enables software to build a model with many assumptions about the user’s knowledge)

Clippy

• microsoft produced an “office assistant” to help users with Microsoft Office

• it was considered very annoying and intrusive by users

• it used a simple user model and probabilistic (baysian) reasoning to decide what to do

Personalised Advertising

• Find the "best match" between a given user in a given context and a suitable advertisement.

• Examples – Context = Web search results -- Sponsored search – Context = Web page -- Content, banners – Other contexts: mobile, video, news, etc

• “Computational Advertising” is a field of study that involves information retrieval, statistical modeling, machine learning, recommender systems, user modelling, game theory…..

Advertising

The financial scale for computational advertising is huge

New Business Opportunity

Returning to online dating

• Recommending people to people– Differs from recommending things eg books

• Scammers• Reliability of data user provides about themself

– eg height, weight, hair colour, age…• And about others

– Issues of self-awareness• Eg. how important is age to you?

– What can you afford to honestly say• Eg religion, race

Light bars – people clicked to show send expression of interest and attribute matches stated preferencesDark bars are where they do not eg 10% do not match stated age preference, 30% do not match religion preference

Why is this so?How to give recommendations in this situation?

L. Pizzato, et al. Recommending people to people: The nature of reciprocal recommenders with a case study in online dating. User Modeling and User-Adapted Interaction: The Journal of Personalization Research, pages 1-42, 2012.

User control and personalisation

Interfaces onto user modelsControl over personalisation

HCI and recommenders

• Huge progress in algorithms for recommenders• Next challenges are interfaces

– A/B testing of layout• eg If there are 5 recommendations, are people more likely to

click on the 1st? 3rd? 5th?• eg. If there are 10 recommendations, are people more likely

to click on one than when there are just 5? – Is it better to have more recommendations on one screen? – Or is it overwhelming?

– Value of explanations for recommendations• enhance trust in recommendations

“Open” user model

• Create an interface onto the user model• Help the user see what the system “thinks

about them”• Enable them to alter the model?• Do people care?• Can we create interfaces that enable them to

do this?

User model display

a user model can be as simple as a list of items (concept, preferences etc) and their value (true/false, percentage etc)

or very complex with a network of concepts.

Data mining in course management systems: Moodle case study and tutorial Cristobal Romero, Sebastian Ventura, Enrique Garcıa Computers & Education 51 (2008) 368–384

Barbara Kump, Christin Seifert, Guenter Beham, Stefanie N. Lindstaedt, and Tobias Ley. 2012. Seeing what the system thinks you know: visualizing evidence in an open learner model. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK '12), Simon Buckingham Shum, Dragan Gasevic, and Rebecca Ferguson (Eds.). ACM, New York, NY, USA, 153-157. DOI=10.1145/2330601.2330640 http://doi.acm.org/10.1145/2330601.2330640

Christopher Brooks, Wengang Liu, Collene Hansen, Gordon McCalla, Jim Greer

Zapata-Rivera, Juan-Diego, and Jim E. Greer. "Interacting with inspectable bayesian student models." International Journal of Artificial Intelligence in Education 14.2 (2004): 127-163

TRAC• open source tool for supporting software development projects

Wiki page editorTicket ManagerSVN source repository

• Not a learning system but used in a learning context.

Mirroring amount of activity

•Each student has consistent colour, clock position•Closer to centre is more work•Logarithmic scale

Activity radar

• Same location of people as in activity diagram• Black is source - blue is sink

Interaction diagram

Team Leader

Narcissus

43

Header –Group view Display for

one user

Time – activity on that day is shown for each user, on each medium

44

Wiki contribitions

svn contribitions

ticket contribitions

45

Click on cell …

…to see details

Another example visualisation for displaying user activity in a user interface was “Patina: Dynamic Heatmaps for Visualizing Application Usage” by researchers at Autodesk in Canada.

• Open user model – selected by user• Can display personal data and “community data”• Logging with accessibility tools• 8 users over one week, 8,742 total click events, activated

a total of 285 times: 92 for the personal over- lay, 130 for the community overlay, and 63 times using the combined data.

User models, privacy, data protection and disclosure, filter bubbles

Personal data, privacy and filter bubbles

Applications capture large amounts of data about users.This is used to personalise interaction. But users may feel that this data is their private property and object to it being released.

Data Ownership

• Information collected about people by one organisation is sometimes sold to another organisation– Eg your browsing habits might be sold

• Information about you can now easily be passed from one organisation to another

• Information about you now persists, potentially forever!

• Facebook comments or photos you posted when you were a teenager may be found by a potential employer when you are 21

• Once you release information into the “wild” it is almost impossible to retract it.

52

Permission

• Some information is given knowingly and with permission to use it elsewhere.

• However, a lot of data is now acquired about people without their (informed) consent.

• People leave digital footprints wherever they go:– Phone calls– Smart phone tracking– Purchases with a credit card– ATM use– Transport card use– Internet use (cookies etc)

• All of this data might be used to personalise a web page with ads etc

53

Filter bubbles• The personalisation process can have

undesirable side-effects:– Always deliver things like those you have most

used in the past• eg always get the same viewpoint in news articles

– May involve such complex processes the user cannot easily discover them

– Much less control them– Loss of serendipity

• “The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think” by Eli Pariser

Summary

• user characteristics, preferences, goals, etc may be represented in a user model

• dynamic user models add another dimension to human-computer interfaces– allowing personalised interaction

• the user interface onto the model itself is very important

• privacy and “filter bubbles” are key concerns