Activity based serendipitous recommendations with the magitti mobile leisure guide
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Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure Guide. V. Bellotti , B. Begole , et al . CHI 2008 Proceedings, pp. 1157-1166. Motivation & Introduction. Motivation Traditional city guide “Time Out” in London and New York, and “Tokyo Walker” in Tokyo

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Activity based serendipitous recommendations with the magitti mobile leisure guide

Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure Guide

V. Bellotti, B. Begole, et al.

CHI 2008 Proceedings, pp. 1157-1166

Motivation introduction
Motivation & Introduction

  • Motivation

    • Traditional city guide

      • “Time Out” in London and New York, and “Tokyo Walker” in Tokyo

    • Location-based services

      • Search for local restaurants, movies, stores and so on

    • Discovery of activities and venues in context-aware computing

  • Magittiproject

    • Sponsored by Dai Nippon Printing Co., Ltd. (DNP)

      • DNP, one of Japan’s largest printing companies

    • Development of a service to replace printed city guides

      • An activity-centered mobile leisure-time guide

      • Delivering timely and personally relevant recommendations about nearby venues

      • Predicting future activity based on the user’s context and models of past behavior

    • Target

      • People : 19~25 year-olds

      • Locations : Japanese cities that have so many venues

Understanding leisure time priorities
Understanding Leisure Time Priorities

  • Dearth of English literature on Japanese leisure time activities

    • Previous time-based survey → too coarse activity for specific recommendation

  • Field exercises for following questions

    • How do young Japanese spend their leisure time?

    • What resources do they use to support leisure time?

    • What needs exist for additional support by a new kind of media technology?

  • Methods for field exercises

    • Interviews and Mockups (IM)

      • 20 semi-structured interviews with 16~33 year olds

      • 12 interviews with 19~25 year olds

    • Online Survey

      • A survey on a market research web site to get statistical information

      • 699 responses from 19~25 year olds

    • Focus Groups

      • 6~10 participants for each group

      • Presentation about a walkthrough of the Magitti mock-up and its functions

      • Gathering detailed feedback on the concept

Understanding leisure time priorities1
Understanding Leisure Time Priorities

  • Methods for field exercises

    • Mobile Phone Diaries (MPD)

      • Daily activities of 19~25 year olds

      • Two mobile phone diary studies

        • First study → 12 people for one Sunday

        • Second study → 21 participants for a seven-day week

    • Street Activity Sampling (SAS)

      • 367 short interviews with people in target age range

        • Reporting three activities from their day

        • Choosing one as a focal activity

        • Classifying the activity into one of a number of pre-determined types

    • Expert Interviews

      • Three experts on the youth market in the publishing industry

      • Information commonly published to inform and support their activities

    • Informal observation

      • Observing young adults in popular Tokyo neighborhoods at leisure

Critical findings from field exercises
Critical Findings from Field Exercises

  • How young people in Tokyo spend their leisure time ?

    • Shopping >going out with friends > dining out > going on a date > doing sport

    • Activity frequency in SAS interviews

      • Dining (31.8%), shopping (24.6%), browse/explore/look (7.5%)

    • Dining and shopping are major activities that involve going out

  • What resources are used to support leisure time ?

    • Friends and family, TV, Internet, and Magazines

      • Online survey respondents → Internet

      • IM interviewees → Friends and family

    • Information based on personal experiences of friends and family

      • Most trusted but not very extensive

  • What needs exist for additional support ?

    • 58.8% of SAS interviewees : interest for more information to support focal activity

    • Requests for information

      • Maps and venue locations (14.6%)

      • Customers’ and friends’ opinions (8.2%)

      • Prices (7.8%)

      • Store/venue contents (6.8%)

Design requirements
Design Requirements

  • Relaxation, Serendipity and Spontaneity

    • Relaxation

      • Busy schedules, often with multiple occupations (e.g., student and part-time-worker)

    • Serendipity

      • Attraction to serendipitous information

  • Avoidance of Information Overload

    • Reducing leisure information to only the most relevant

  • Minimal size

    • Particular preference of the younger generations

      • As small as possible in a pocket

  • One-handed operation

    • Strong requirement for one-handed operation by interviewees

  • Focusing on relaxation, serendipity, and spontaneity

    • Generating recommendations automatically using activity inference

Magitti design
Magitti Design

  • Magitti

    • Context filtering to reduce overload of leisure time in dense urban areas

    • No requirement of explicit definition of a user’s profile or preferences

    • Inference of interests and activities from learned models

      • Using data such as places visited, web browsing, and communications with friends

  • Magitti’s three key features

    • Context Awareness

      • Using current time, location, weather, store hours, and user patterns

    • Activity Awareness

      • User’s inferred or specified activity based recommendation

      • Eating, Shopping, Seeing, Doing, or Reading

    • Serendipitous, relaxing experience

      • Not necessary for profile, preferences, or queries

      • Activity inference for Magitti using context

Related activity detection research
Related Activity-Detection Research

  • Lamming and Newman’s activity-based information-retrieval system

    • One early system related to Magitti

    • Presentation of information that was generated in contexts

    • Impossible to infer activity with effective accuracy

  • Other activity detection approaches

    • Begole et al. : sensor-based availability detection

    • Inference of human activities from use of objects with RFID tags

    • Inference of human activities by using video and audio data analysis

    • Froehlich et al. : finding correlations between place preference and data

  • Activity modeling research

    • Liao et al. using location-based sensing with Relational Markov Networks

      • ‘AtHome’, ‘AtWork’, ‘Shopping’, ‘DiningOut’, and ‘Visiting’

  • Previous works

    • Detection of a person’s current activity

    • Magitti guide system : predicting a person’s future activities

Related mobile city guide applications
Related Mobile City Guide Applications

  • Location-based information recommendation system

    • Similar in spirit to Magitti: location-aware tourist guides

  • Some systems to recommend venues based on the user’s state

    • No prediction of the user’s activities

  • Cyberguide

    • A mobile tourist guide for the Georgia Tech campus

    • Awareness of its time, location, and history

    • Matching information on venues and special events to the data

  • MobyRec

    • A context-aware mobile tourist recommender system

      • Hotels, restaurants, etc.

    • Improvement of recommendations over time

Related mobile city guide applications1
Related Mobile City Guide Applications


    • Providing tour routes and accesses ticket reservation services

    • Dynamically recomputing routes based on location and time

    • Targeting for touring unfamiliar areas


    • A tourist guide service covering a wide range of venue types

    • Using profile and goal information entered by its user

    • Using location, speed, user profile, schedule, shopping list, and recent visit

    • Filtering by the user’s stated goal and preferences


    • Providing tips, tour suggestions, maps and other information on a range of tourist-related venues (restaurants, movies, shows, etc.)

    • Learning user preferences over time

Magitti user interface
Magitti : User Interface

  • Main Screen

    • A scrollable list of up to 20 recommended items in Main Screen

      • Matching the user’s current situation and profile

    • Automatic list update to show items relevant to new locations

  • Detail Screen

    • Viewing Detail Screen by tapping each recommendation

      • Initial texts of a description, a formal review, and user comments

      • Rating the item on a 5-star scale by a user

Magitti user interface1
Magitti : User Interface

  • Partial map on the Main Screen

    • Showing the four items currently visible in the list

  • Minimal size and one-handed operation requirements

    • Large buttons on the screen to enable the user to operate Magitti with a thumb

    • Marking menus on touch screens to operate the interface

  • Menu buttons at the bottom of the Main Screen

    • Adjusting the recommendation list if needed

    • Five modes of user activity; Eat, Buy, See, Do, or Read

    • Recommendations from just one category

    • Bookmarking recommended items

Magitti system architecture
Magitti : System Architecture

  • Client-server architecture

  • Mobile client UI on a handheld device

    • Providing data for the Context Sensing Module

  • Gathering data about user’s physical context and data context

    • User’s physical context

      • GPS, time of day, user inputs, weather

    • Data context

      • Content of emails sent/received, calendar, web pages and documents viewed, applications used

Magitti activity prediction module
Magitti : Activity Prediction Module

  • Data for probabilistic modeling

    • Using data collected on Magitti’s target demographic in the fieldwork

    • Japanese Survey on Time Use and Leisure Activities

  • Modeling the frequency of each mode by tracking user behavior

    • Visiting a retail store → Buy

    • Visiting a restaurant or café → Eat

    • Visiting theater or museum → See

    • Gym or park → Do

    • Reading of content on Magitti itself → Read

Magitti recommender system
Magitti : Recommender System

  • Computation of the utility of each content item

    • Combining results from a variety of recommendation models

    • After computing scores of all items, top results are allocated in the slot

  • Computing score for an item in Magitti

    • Combination of Eight Model

Computing score for an item in magitti
Computing score for an item in Magitti

  • Collaborative filtering

    • Computing similarities between users

    • Determining scores each item based on how other similar users rated it

  • Stated Preferences

    • Scoring items according to how closely they match the user’s stated preferences

  • Learned Preferences

    • Learning from observed behavior rather than explicitly stated preferences

  • Content preference

    • Measuring the similarity of an item’s content to a user’s profile

  • Distance

    • Items within a distance range (either entered or inferred from location traces)

  • Reading

    • Using a model of users from the fieldwork

  • Boredom Buster

    • Reducing scores of items that have previously been seen

  • Future Plans

    • Temporarily raising scores based on future plans derived from the Content Analysis

Data context detection
Data Context Detection

  • Detecting the user’s physical context

    • Calendar appointments, viewed documents, and messages to extract information about the user’s plans

    • Leisure activity plans with friends using mobile email and SMS

  • Test for the potential usefulness of SMS

    • 10,000 SMS messages by students at the National University of Singapore

    • 11% of the messages related to leisure activities

  • Prototype Content Analysis module

    • Only Eat and See activity planning

    • Other activities planned for future work

Field evaluation
Field Evaluation

  • 11 volunteers with Magitti in the Palo Alto, California area between one and four times each over several days

    • Participants, who were company employees not working on the project, ranged in age from mid-20s to late-50s, and averaged 37

  • Visiting a total of 60 places over 32 outings, averaging 1.9 places per outing.

    • About half the outings (16) accompanied by a family member or friend

Supporting serendipity
Supporting Serendipity

  • Try to find a new place(such as restaurant)

    • Very successful at discovering new places

    • Over half new places (53%)

      • Including 38% that they had never heard of

      • Including 15% they had heard of but never been to

    • Places visited once or twice (25%)

    • Places visited many times (23%)

  • People’s expression about finding new places

    • “Cool! I like that. I would never have found that place if it wasn't for this.”

    • “I think 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.”

  • Magitti’s overall usefulness

    • 4.1 on a scale of 1-5 (5=very helpful)

    • Useful for residents and not just tourists or newcomers

Predicting user activity
Predicting User Activity

  • User activities in the experiment

    • Visiting 30 places to Eat, 27 to Buy, and 3 to Do

    • Some most frequent activities

  • Changing activity type : an average of 5.1 times per outing

    • Eat (1.8 times per outing), Buy (1.4), Do (0.7), See (0.5), and Read (0.1)

    • “Any” mode : an average of 0.7 times per outing

  • Wrong inference : easy to switch to a different activity

Context aware recommendations
Context-Aware Recommendations

  • Relevant and interesting recommendations

    • Average rating of 3.8 (1=rarely, 5=almost always)

      • A little less than “usually”

    • A person’s opinion

      • “Most of the time, the list contained a mix of useful and not so useful recommendations”

  • Several factors that affected people’s confidence in the system

    • Omission

      • “the list did not represent what downtown has to offer”

      • Small omissions or inaccuracies reduced people’s trust

    • Distance

      • People expect that the closest places would be at the top of the list

      • Poor recommendation if it required driving

    • First Item

      • More weight on the first item recommended

      • Reasonable first item → good recommendations

    • Guide vs. Recommender

      • Relatively less loss of confidence for recommendation of a closed place

        • Some people : location information guide (closest place)

        • Other people : recommender (similar place)

Context aware recommendations1
Context-Aware Recommendations

  • Several factors that affected people’s confidence in the system

    • Transparency

      • Some users try to understand how Magitti decided which activities and venues to list

      • A complex set of algorithms based on many factors

        • Location, time, preferences, similar users’ opinions, prior behavior

      • Lack of transparency of the algorithm

        • sometimes confusing or even frustrating users

      • Need for offering more cues to help users develop an appropriate user model

Issues conclusion
Issues & Conclusion

  • User Control

    • Desire to have more control in managing the recommendation list

    • Ability to sort the items by factors such as rating, price, or distance

    • Ability to remove items from the list

  • Social Use

    • Outings involved two or more people

    • Incorporation into a social setting

  • Conclusion

    • Predicting the user’s current and future leisure activity

    • Modeling the user’s preferences, to filter and recommend relevant content

    • An interface with a novel one-handed, thumb based interaction