activity-based serendipitous recommendations with the magitti mobile leisure guide
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System Codename: MagittiDesigned and Prototyped by PARC forDai Nippon Printing Co. Ltd.
Presenters• Victoria Bellotti• Bo Begole• Ellen Isaacs
The Other Co-authorsEd H. Chi, Nicolas Ducheneaut, Ji Fang,Tracy King, Mark W. Newman, Kurt Partridge, Bob Price, Paul Rasmussen, Michael Roberts, Diane J. Schiano, Alan Walendowski
Activity-Based Serendipitous Recommendations Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure Guidewith the Magitti Mobile Leisure Guide
2
Overview
•Background and motivating fieldwork
•System design
•Evaluation
Recommendation Server
ConsumerLocal Area
Context: Time, Location, etc. Restaurants, stores,
events, etc.
Mobile Device
Preferences:Sushi, Bookstores,
etc.
Filter and Rank Database Items
Infer Activity
FeedbackFeedback
Model Preferences
3
About Dai Nippon Printing Co. Ltd.
• DNP is a world leader in printing technology and solutions
• Affected by the shift from paper to digital media
The Past: People carried magazines The Present: Most Japanese use a mobile
phone to browse the Web and read/write E-mail
• DNP asked PARC to develop core technology for new, consumer-friendly digital media
• All design to be driven by real need motivated a lot of work to identify:
• Best target users• Best solution for their needs
Traditional Publishing
Modern Publishing
4
Fieldwork 2 Confirm
and Refine
Fieldwork 2 Confirm
and RefineEvaluate design mock-up in situ
Refine design based on user feedback
Fieldwork 1 Choose
Best Idea
Fieldwork 1 Choose
Best IdeaInterviews,
observations, and scenario
feedback
Analyze results
Refine concept design
Future technology
analysis
Finalized Concept Proposal
Finalized Concept Proposal
Leisure guide concept
proposal,“Magitti”
Contextual Publishing Concept Development
Technology BrainstormTechnology Brainstorm
Personas bring customer to life
Share background domain info
Brainstorm design ideas
Discover Target Users
Discover Target Users
Assess many markets
Develop scenarios and obtain feedback
Choose the best
Young Adults at Leisure
Activity-Aware Leisure Guide
What to Build
5
Many User Studies During Concept Development and Early System Development
Observation Focus Groups
Activity Sampling
In-depth interviews
Mobile-phone Diaries
NotesDiary entriesLocation
Survey responses
1000’s of Photos 40 Transcripts 10 Transcripts
Time Time
Fashion
Identity
Technology use
Transportation
Leisure activity type frequency
Leisure activity venue types popularity
Leisure activity type timing & probability
3000 activity & time reports
370 activity, time & location reports
Planning
Media use
Information sources
Information desired
Social factors in leisure
Knowledge of locale
Observation reminders Practices Needs Priorities
Surveys
670 Responses
Problems
Classifying CountingCodingCorrelating
Leisure activity type locations
Coordination
Activity type prediction
Leisure activity types
Form-factorFeatures
FunctionsInteraction style
Venue database classification
Content
Study MethodsStudy Methods
Informing Design ofInforming Design of
DataData
Analysis: Analysis: increasing abstractionincreasing abstraction
6
From Fieldwork: Who Are the Users
• Japanese youth are especially receptive to new technology
• 19-25 year-olds spend 1.5 times more time in leisure activities than 16-19 year-olds or 26-33 year-olds
• Less school and work pressure• Ideal target for our design
• Still very, very busy• School, jobs and little sleep• Relaxation is a priority• The system should do the work
• Want to know what others think• Value opinions of real people• Include end-user content
7
From Fieldwork: What do they Do?
• Outings often involve meeting friends• Often at “halfway point” far from
homes
• Eager for local and localized info• Unfamiliar with locations they visit
• Open to suggestions• May not plan the main activity• May not plan follow-on activities
• Motivation for Magitti• A city-guide that assists in
exploration
0
10
20
30
40
50
60
1 2 3 4 5 6 7
1 = Not at All 7 = Extremely Well
Ratings of “How well I know this neighborhood”
given by 170 young people stopped on the streets in diverse neighborhoods in Tokyo
8
Overview
•Background and motivating fieldwork
•System design
•Evaluation
Recommendation Server
ConsumerLocal Area
Context: Time, Location, etc. Restaurants, stores,
events, etc.
Mobile Device
Preferences:Sushi, Bookstores,
etc.
Filter and Rank Database Items
Infer Activity
Model Preferences
9
User Interface
Map
Pie Menu Details
10
Demo Videohttp://www2.parc.com/csl/groups/ubicomp/videos/magitti_project_demonstration.wmv
Recommendable Items
Restaurant ReviewsStore DescriptionsParks Descriptions
Movie ListingsMuseum Events
Magazine Articles…
Recommendable Items
Restaurant ReviewsStore DescriptionsParks Descriptions
Movie ListingsMuseum Events
Magazine Articles…
EAT Straits Cafe 0.77
EAT Fuki Sushi 0.64
SEE J. Gallery 0.60
EAT Tamarine 0.57
DO Sam’s Salsa 0.39
EAT Bistro Elan 0.38
BUY Apple Store 0.33
EAT Spalti 0.31
Filteringand
Ranking
Filteringand
Ranking
Activity UtilityInformation
Recommendable Items
Restaurant ReviewsStore DescriptionsParks Descriptions
Movie ListingsMuseum Events
Magazine Articles…
Recommendable Items
Restaurant ReviewsStore DescriptionsParks Descriptions
Movie ListingsMuseum Events
Magazine Articles…
EAT Straits Cafe 0.77
EAT Fuki Sushi 0.64
SEE J. Gallery 0.60
EAT Tamarine 0.57
DO Sam’s Salsa 0.39
EAT Bistro Elan 0.38
BUY Apple Store 0.33
EAT Spalti 0.31
Filteringand
Ranking
Filteringand
Ranking
Activity UtilityInformation
ContextContext• Time Time • LocationLocation• Email analysisEmail analysis• Calendar analysisCalendar analysis
HistoryHistory• Prior population Prior population
patternspatterns• User QueriesUser Queries• User Locations User Locations
Eat 35%Buy 20%See 25%Do 10%Read 10%
What you are doing now
Recommendable Items
Restaurant ReviewsStore DescriptionsParks Descriptions
Movie ListingsMuseum Events
Magazine Articles…
Recommendable Items
Restaurant ReviewsStore DescriptionsParks Descriptions
Movie ListingsMuseum Events
Magazine Articles…
EAT Straits Cafe 0.77
EAT Fuki Sushi 0.64
SEE J. Gallery 0.60
EAT Tamarine 0.57
DO Sam’s Salsa 0.39
EAT Bistro Elan 0.38
BUY Apple Store 0.33
EAT Spalti 0.31
Filteringand
Ranking
Filteringand
Ranking
Activity UtilityInformation
What you likeWhat
you like
Personal PreferencesPersonal Preferences• Explicit preferencesExplicit preferences• Ratings of placesRatings of places• Topics of documents readTopics of documents read• Behavior; where/when/whatBehavior; where/when/what
ContextContext• Time Time • LocationLocation• Email analysisEmail analysis• Calendar analysisCalendar analysis
HistoryHistory• Prior population Prior population
patternspatterns• User QueriesUser Queries• User Locations User Locations
Eat 35%Buy 20%See 25%Do 10%Read 10%
What you are doing now
Recommendable Items
Restaurant ReviewsStore DescriptionsParks Descriptions
Movie ListingsMuseum Events
Magazine Articles…
Recommendable Items
Restaurant ReviewsStore DescriptionsParks Descriptions
Movie ListingsMuseum Events
Magazine Articles…
EAT Straits Cafe 0.77
EAT Fuki Sushi 0.64
SEE J. Gallery 0.60
EAT Tamarine 0.57
DO Sam’s Salsa 0.39
EAT Bistro Elan 0.38
BUY Apple Store 0.33
EAT Spalti 0.31
Filteringand
Ranking
Filteringand
Ranking
Activity UtilityInformation
What you likeWhat
you like
Personal PreferencesPersonal Preferences• Explicit preferencesExplicit preferences• Ratings of placesRatings of places• Topics of documents readTopics of documents read• Behavior; where/when/whatBehavior; where/when/what
ContextContext• Time Time • LocationLocation• Email analysisEmail analysis• Calendar analysisCalendar analysis
HistoryHistory• Prior population Prior population
patternspatterns• User QueriesUser Queries• User Locations User Locations
Eat 35%Buy 20%See 25%Do 10%Read 10%
What you are doing now
15
Predicting Activities from Population Priors
Mobile-phone Diaries
Hourly activity report:• Who• Where• When• What• Info used & desired
Code each respondent’s activities over 7-day week
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Time of Day
0
5
10
15
20
Sa
mp
le C
ou
nt
(To
tal)
NOTSEEDOEAT OUTSHOP
Friday
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Time of Day
0
5
10
15
20
Sa
mp
le C
ou
nt
(To
tal)
NOTSEEDOEAT OUTSHOP
Sunday
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Time of Day
0
5
10
15
20
25
Sa
mp
le C
ou
nt
(To
tal)
NOTSEEDOEAT OUTSHOP
Saturday
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Time of Day
0
10
20
30
40
50
60
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Sa
mp
le C
ou
nt
(To
tal)
NOTSEEDOEAT OUTSHOP
Mon-Thu
Predict probability of each activity type
Aggregate all dataWhen there is no user-specific When there is no user-specific
data, prior population data is useddata, prior population data is used
16
Predicting Activities from Email/SMS• How well do messages suggest activity?
• We examined a public set of 10,000 SMS messages from National University of Singapore students, similar to the Magitti target demographic
• Approximately 11% of the messages contain information related to leisure activities
tomorrow what time you be in school? think me and shuhui meeting in school around 4. then duno still can see movie or not because duno if a rest want meet for dinner.
• Keywords and linguistic structures are identified and sent to the activity inference mechanism
ACTCAT=SEE, EAT :: ACTTIME=2007/05/26 16:00 :: UNCERTAINTY=10 minutes :: TENSE=FUTURE
17
Learning Individual Patterns
Date/Time Location Address Venue Name
Venue Type
Activity Class
Sun, 27 Jan 200811:57- 12:45
37°26’39”-122°9’38”
389 Ramona Evvia Restaurant EAT
Tue, 29 Jan 20081:22 - 1:31
37°23’11”-122°9’02”
545 Hamilton,
Brickworks Cafe EAT
Wed, 30 Jan 200811:57- 12:45
37°26’39”-122°9’18”
143 Quarry Road
Walgreens Store SHOP
Fri, 1 Feb 200813:11 - 13:37
37°24’11”-122°9’00”
854 University
Restoration Hardware
Store SHOP
… … … … …
0 2 4 6 8 10 12 14 16 18 20 22 24
Shopping Center
0 2 4 6 8 10 12 14 16 18 20 22 24
Downtown
EAT MostLikely
SHOP MostLikely
Undetermined
Time Individualized
pattern by region
18
Activity Inference Evaluation
Magitti Accuracy on Palo Alto Field Evaluation Data
62%
77%82%
0%
20%
40%
60%
80%
100%
Baseline (EAT) Time and Place Priors Priors + Learning
* Time and Place Priors is significantly different than Baseline (Chi Square p=0.014, McNemar p=0.048).† Priors + Learning is significantly different than Baseline (Chi Square p=0.0027, McNemar p=0.008).
* †
19
Overview
•Background and motivating fieldwork
•System design
•Evaluation
Recommendation Server
ConsumerLocal Area
Context: Time, Location, etc. Restaurants, stores,
events, etc.
Mobile Device
Preferences:Sushi, Bookstores,
etc.
Filter and Rank Database Items
Infer Activity
Model Preferences
20
Preliminary Field Evaluation
• 11 people, 32 outings (2.9 per person)• Shadowed one outing per participant
• 60 places visited (1.9 per outing)• 30 restaurants, 27 shops, 3 parks
• 16 outings accompanied by companion(s)
Using Magitti in a demo
21
Overall Usefulness
• Usefulness• Average of 35.0 recommendation list pages viewed per outing• People rated “helpfulness” 4.1 on 5-point scale (5 high)
• "Cool! I like that. I would never have found that place if it wasn't for this.”
• "It makes life more interesting. It allows you to get out of your daily routine, almost as if you’re going to a different city.”
• Serendipitous Discovery• 53% of places visited were new to the participants• On 67% of outings they went to at least one new place • On 69% of outings, they noticed another new place to visit later
22
User Response
• Predicting User Activity• People changed activity 5.1 times per outing • “It’s very nice that it recommends things without you
having to do anything, but sometimes you want to ask for specific things.”
• Even when Magitti got it right, they still sometimes switched, apparently because they wanted all the recommendations to be for that activity
• Social Use• Five of eight users reported difficulty in sharing
experience with another person• Magitti user seen as disconnected from others and/or
controlling the outing
23
Quality of Recommendations
• Recommendations rated 3.8 on 1-5 scale of "relevant and of interest“
• "Most of the time, the list contained a mix of useful and not so useful recommendations“
• Biggest factors to reduce confidence in recommendations
• Not seeing a nearby place in the list• Getting recommendations for places too far away• Lack of transparency of reasons for recommendations
24
• Information and suggestions based on
• Situation• Past behavior• Personal preferences
Replace Tedious Mobile Searching with Personalized Recommendations
Stop searching!
Let information find you!
Victoria Bellotti, Bo Begole, Ed H. Chi, Nicolas Ducheneaut, Ji Fang, Ellen Isaacs, Tracy King, Mark W. Newman, Kurt Partridge, Bob Price, Paul Rasmussen, Michael Roberts, Diane J. Schiano, Alan Walendowski
Thanks also to: Ame Elliott and Dai Nippon Printing
25
Supplemental Slides
50%
50%
Venue Likelihood:
1:00
Monday Tuesda
…
12:00 to 1:00
1:00 to
Hector’s CafeAstrid’s Grocery
12:00
Time Location Visit
11:57- 12:45 37°26’39”-122°9’38”
1:22 - 1:31 37°23’11”-122°9’02”
… … …
Context HistoryContext HistoryWeekly Behavior PatternsWeekly Behavior Patterns
$$$
GroceryCafe
…
…
…
$$$
GroceryCafe
…
…
Predicting Activities fromLearned User Patterns
BUYEAT
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