• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Rethinking Mobile Recommendations
 

Rethinking Mobile Recommendations

on

  • 2,519 views

Recommending Social Events from Mobile Phone Location Data...

Recommending Social Events from Mobile Phone Location Data

A city offers thousands of social events a day, and it is difficult for dwellers to make choices. The combination of mobile phones and recommender systems can change the way one deals with such abundance. Mobile phones with positioning technology are now widely available, making it easy for people to broadcast their whereabouts; recommender systems can now identify patterns in people’s movements in order to, for example, recommend events. To do so, the system relies on having mobile users who share their attendance at a large number of social events: cold-start users, who have no location history, cannot receive recommendations. We set out to address the mobile cold-start problem by answering the following research question: how can social events be recommended to a cold-start user based only on his home location?
To answer this question, we carry out a study of the rela- tionship between preferences for social events and geography, the first of its kind in a large metropolitan area. We sample location estimations of one million mobile phone users in Greater Boston, combine the sample with social events in the same area, and infer the social events attended by 2,519 residents. Upon this data, we test a variety of algorithms for recommending social events. We find that the most effective algorithm recommends events that are popular among residents of an area. The least effective, instead, recommends events that are geographically close to the area. This last result has interesting implications for location-based services that emphasize recommending nearby events.

Statistics

Views

Total Views
2,519
Views on SlideShare
1,491
Embed Views
1,028

Actions

Likes
1
Downloads
23
Comments
0

5 Embeds 1,028

http://mobblog.cs.ucl.ac.uk 548
http://www.cl.cam.ac.uk 475
http://www.cs.ucl.ac.uk 3
http://profzero.org 1
http://researchswinger.org 1

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Rethinking Mobile Recommendations Rethinking Mobile Recommendations Presentation Transcript

    • _ nets & the city _
    • Use mobility data …
    • … to recommend social events
    • mobile phone location data
    • location estimations lessons
    • 1. infer attendace at events 2. recommend in 6 ways location estimations lessons 3. measure “quality”
    • time distance 1m users (20% population) sample 80K 1. infer attendace at events location estimations attendance
    • distance 1. infer attendace at events location estimations attendance
    • time 1. infer attendace at events location estimations attendance
    • resolutions: time (1 ½ h) & space (350m) 1. infer attendace at events location estimations attendance
    • 1. infer attendace at events location estimations attendance
    • it’s not about single individuals . it’s about areas 1. infer attendace at events location estimations attendance
    •  
    • On input of area of residence : 1. popular events 2. geographically close 3. popular in area of residence 4. TF-IDF (similar to 3 expect for less-attended events) 5. K-Nearest Locations 6. K-Nearest Events 2. recommend in 6 ways attendance ranked recommendations
    • Shakespeare Red Sox You went to… lessons 3. measure “quality” ranked recommendations
    • Shakespeare Red Sox You went to… lessons 3. measure “quality” ranked recommendations
    • Shakespeare Red Sox You went to… 1. Shakespeare 2. Cirque … 5. Red Sox 1. Shakespeare 2. Red Sox … 5. Cirque lessons 3. measure “quality” ranked recommendations
    • Shakespeare Red Sox You went to… 1. Shakespeare 2. Cirque … 5. Red Sox 1. Shakespeare 2. Red Sox … 5. Cirque average percentile ranking High Low lessons 3. measure “quality” ranked recommendations
    • lessons 3. measure “quality” ranked recommendations
    • Lesson 1: geographically close isn’t the best ;-) lessons 3. measure “quality” ranked recommendations
    • lessons 3. measure “quality” ranked recommendations
    • Lesson 2: popular in area rocks ;-) lessons 3. measure “quality” ranked recommendations
    • lessons 3. measure “quality” ranked recommendations
    • Lesson 3: geographical patterns matter ;-) lessons 3. measure “quality” ranked recommendations
    • geographical patterns matter geographically close isn’t the best ‘ popular in area’ rocks
    • Future
    • Future 1| differential privacy
    • SpotME if you can fake your location yet aggregate location data is still OK
    • promoting location privacy… one lie at a time
    • Future 2| social nets & space
    •