Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure Guide

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This paper presents a context-aware mobile recommender system, codenamed Magitti. Magitti is unique in that it infers user activity from context and patterns of user behavior and, without its user having to issue a query, automatically generates recommendations for content matching. Extensive field studies of leisure time practices in an urban setting (Tokyo) motivated the idea, shaped the details of its design and provided data describing typical behavior patterns. The paper describes the fieldwork, user interface, system components and functionality, and an evaluation of the Magitti prototype.

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Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure Guide

  1. 1. Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure Guide System Codename: Magitti Designed and Prototyped by PARC for Dai Nippon Printing Co. Ltd. Presenters • Victoria Bellotti • Bo Begole • Ellen Isaacs The Other Co-authors Ed H. Chi, Nicolas Ducheneaut, Ji Fang, Tracy King, Mark W. Newman, Kurt Partridge, Bob Price, Paul Rasmussen, Michael Roberts, Diane J. Schiano, Alan Walendowski
  2. 2. Overview Recommendation Server Infer Activity Filter and Rank •Background Model Preferences Database Items and motivating Context: Time, Location, etc. fieldwork Restaurants, stores, events, etc. Preferences: Sushi, Bookstores, etc. •System design Mobile Device Feedback •Evaluation Local Area Consumer 2
  3. 3. About Dai Nippon Printing Co. Ltd. • DNP is a world leader in printing technology and solutions • Affected by the shift from paper to Traditional digital media Publishing 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 Modern • Best solution for their needs Publishing 3
  4. 4. Contextual Publishing Concept Development Discover Technology Fieldwork 1 Fieldwork 2 Finalized Target Brainstorm Choose Confirm Concept Users Best Idea and Refine Proposal Assess many markets Share background Interviews, Evaluate design Future domain info observations, mock-up in situ technology and scenario analysis feedback Personas bring customer to life Develop scenarios and obtain feedback Leisure guide concept proposal, “Magitti” Brainstorm design ideas Analyze results Refine Refine design based concept on user feedback design Choose the best Young Adults Activity-Aware What to Build 4 at Leisure Leisure Guide
  5. 5. Many User Studies During Concept Development and Early System Development Features Content Interaction Venue database Activity type Informing Design of Form-factor Functions style classification prediction Identity Social factors Planning Coordination Leisure activity Leisure activity Information in leisure Transportation venue types type timing & Knowledge popularity probability sources of locale Technology use Fashion Information Media Leisure activity Leisure activity type desired use Leisure activity types type locations frequency Correlating Analysis: increasing abstraction Classifying Coding Counting Observation 370 activity, time 3000 activity & reminders Practices Needs Priorities Problems & location reports time reports Survey Time Time Data responses Diary 1000’s of Photos Notes 40 Transcripts 670 Responses 10 Transcripts Location entries Observation In-depth Surveys Focus Activity Mobile- interviews Groups Sampling phone Diaries Study Methods 5
  6. 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 6
  7. 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 60 50 • A city-guide that assists in exploration 40 30 Ratings of “How well I know this neighborhood” 20 given by 170 young people stopped on the 10 streets in diverse neighborhoods in Tokyo 0 1 2 3 4 5 6 77 1 = Not at All 7 = Extremely Well
  8. 8. Overview Recommendation Server Infer Activity Filter and Rank •Background Model Preferences Database Items and motivating Context: Time, Location, etc. fieldwork Restaurants, stores, events, etc. Preferences: Sushi, Bookstores, etc. •System design Mobile Device •Evaluation Local Area Consumer 8
  9. 9. User Interface Pie Menu Details Map 9
  10. 10. Demo Video http://www2.parc.com/csl/groups/ubicomp/videos/magitti_project_demonstration.wmv 10
  11. 11. Activity Information Utility EAT Straits Cafe 0.77 Recommendable Items EAT Fuki Sushi 0.64 Filtering and SEE J. Gallery 0.60 Restaurant Reviews Ranking EAT Tamarine 0.57 Store Descriptions Parks Descriptions DO Sam’s Salsa 0.39 Movie Listings EAT Bistro Elan 0.38 Museum Events Magazine Articles BUY Apple Store 0.33 … EAT Spalti 0.31
  12. 12. Context History • Time • Prior population • Location patterns • Email analysis • User Queries • Calendar analysis • User Locations Eat 35% What you Buy 20% are doing See 25% Do 10% now Read 10% Activity Information Utility EAT Straits Cafe 0.77 Recommendable Items EAT Fuki Sushi 0.64 Filtering and SEE J. Gallery 0.60 Restaurant Reviews Ranking EAT Tamarine 0.57 Store Descriptions Parks Descriptions DO Sam’s Salsa 0.39 Movie Listings EAT Bistro Elan 0.38 Museum Events Magazine Articles BUY Apple Store 0.33 … EAT Spalti 0.31
  13. 13. Personal Preferences Context History • Explicit preferences • Time • Prior population • Ratings of places • Location patterns • Topics of documents read • Email analysis • User Queries • Behavior; where/when/what • Calendar analysis • User Locations Eat 35% What you Buy 20% What are doing See 25% you like Do 10% now Read 10% Activity Information Utility EAT Straits Cafe 0.77 Recommendable Items EAT Fuki Sushi 0.64 Filtering and SEE J. Gallery 0.60 Restaurant Reviews Ranking EAT Tamarine 0.57 Store Descriptions Parks Descriptions DO Sam’s Salsa 0.39 Movie Listings EAT Bistro Elan 0.38 Museum Events Magazine Articles BUY Apple Store 0.33 … EAT Spalti 0.31
  14. 14. Personal Preferences Context History • Explicit preferences • Time • Prior population • Ratings of places • Location patterns • Topics of documents read • Email analysis • User Queries • Behavior; where/when/what • Calendar analysis • User Locations Eat 35% What you Buy 20% What are doing See 25% you like Do 10% now Read 10% Activity Information Utility EAT Straits Cafe 0.77 Recommendable Items EAT Fuki Sushi 0.64 Filtering and SEE J. Gallery 0.60 Restaurant Reviews Ranking EAT Tamarine 0.57 Store Descriptions Parks Descriptions DO Sam’s Salsa 0.39 Movie Listings EAT Bistro Elan 0.38 Museum Events Magazine Articles BUY Apple Store 0.33 … EAT Spalti 0.31
  15. 15. Predicting Activities from 100% Mon-Thu Sample Count (Total) 80 90% 70 80% Population Priors 70% 60 NOT 60% 50 SEE 50% 40 DO 40% EAT OUT 30 SHOP 30% Aggregate 20 When there is no user-specific 20% 10% 10 all data 0% 0 1 0 1 0 1 0 1 0 2 0 0 1 0 1 0 2 0 data, prior population data is used 0 0 0 0 :3 :0 :3 :0 :3 :0 :3 :0 :3 :0 :3 :0 :0 0 2 3 5 6 1 2 8 9 6 7 9 0 1 Time of Day Friday Mobile-phone Code each respondent’s 100% Sample Count (Total) 20 90% Diaries activities over 7-day week 80% 70% 15 NOT 60% SEE 50% 10 DO 40% EAT OUT 30% SHOP 5 20% 10% 0% 0 1 0 1 0 1 0 1 0 2 0 0 1 0 1 0 2 0 0 0 0 0 :3 :0 :3 :0 :3 :0 :3 :0 :3 :0 :3 :0 :0 0 2 3 5 6 1 2 8 9 6 7 9 0 1 Time of Day Saturday 100% 25 Predict Sample Count (Total) Hourly activity report: 90% • Who 80% 70% 20 probability of each NOT • Where 60% 50% 15 SEE DO • When 40% 30% 10 EAT OUT SHOP activity • What 20% 5 type 10% • Info used & desired 0% 0 1 0 1 0 1 0 1 0 2 0 0 1 0 1 0 2 0 0 0 0 0 :3 :0 :3 :0 :3 :0 :3 :0 :3 :0 :3 :0 :0 0 2 3 5 6 1 2 8 9 6 7 9 0 1 Time of Day 100% Sunday Sample Count (Total) 20 90% 80% 70% 15 NOT 60% SEE 50% 10 DO 40% EAT OUT 30% SHOP 5 20% 10% 15 0% 0 1 0 1 0 1 0 1 0 2 0 0 1 0 1 0 2 0 0 0 0 0 :3 :0 :3 :0 :3 :0 :3 :0 :3 :0 :3 :0 :0 0 2 3 5 6 1 2 8 9 6 7 9 0 1 Time of Day
  16. 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 16
  17. 17. Learning Individual Patterns Date/Time Location Address Venue Venue Activity Name Type Class Sun, 27 Jan 2008 37°26’39” 389 Ramona Evvia Restaurant EAT 11:57- 12:45 -122°9’38” Tue, 29 Jan 2008 37°23’11” 545 Brickworks Cafe EAT 1:22 - 1:31 -122°9’02” Hamilton, Wed, 30 Jan 2008 37°26’39” 143 Quarry Walgreens Store SHOP 11:57- 12:45 -122°9’18” Road Fri, 1 Feb 2008 37°24’11” 854 Restoration Store SHOP 13:11 - 13:37 -122°9’00” University Hardware … … … … … Downtown EAT Most Likely 0 2 4 6 8 10 12 14 16 18 20 22 24 SHOP Most Likely Time  Individualized Shopping Center pattern by region Undetermined 0 2 4 6 8 10 12 14 16 18 20 22 17 24
  18. 18. Activity Inference Evaluation Magitti Accuracy on Palo Alto Field Evaluation Data 100% 82% 77% 80% 62% 60% 40% 20% 0% 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). 18
  19. 19. Overview Recommendation Server Infer Activity Filter and Rank •Background Model Preferences Database Items and motivating Context: Time, Location, etc. fieldwork Restaurants, stores, events, etc. Preferences: Sushi, Bookstores, etc. •System design Mobile Device •Evaluation Local Area Consumer 19
  20. 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 20
  21. 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 21
  22. 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 22
  23. 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 23
  24. 24. Replace Tedious Mobile Searching with Personalized Recommendations • Information and suggestions based on • Situation • Past behavior • Personal preferences 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 24
  25. 25. Supplemental Slides 25
  26. 26. Predicting Astrid’s Grocery Activities Hector’s Cafe from EAT BUY Learned User Patterns Venue 50% Likelihood: 50% 12:00 1:00 Weekly Behavior Patterns Context History Monday Tuesda Time Location Visit … … … 11:57- 12:45 37°26’39” 12:00 $ $ -122°9’38” to 1:00 $$ $$ 1:22 - 1:31 37°23’11” Grocery Grocery -122°9’02” Cafe Cafe … … … … 1:00 to … …

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