I’m Feeling LoCo: A Location Based Context Aware ...€¦ · I’m Feeling LoCo: A Location Based...
Transcript of I’m Feeling LoCo: A Location Based Context Aware ...€¦ · I’m Feeling LoCo: A Location Based...
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I’m Feeling LoCo: A Location Based Context Aware Recommendation System
Saiph Savage1, Maciej Baranski1, Norma Elva Chavez2, Tobias Hollerer1
1University of California, Santa Barbara 2Universidad Nacional Autonoma de Mexico
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Recommendation Systems
User Model
Make Recommendations! 2
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Recommendation Systems: Content Based Approach
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User Model
Place Model
Place Model
Make Recommendations!
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Recommendation Systems: Content Based Approach
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Generally requires User to complete extensive surveys
User Model Place Model
Place Model
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Recommendation Systems: Collaborative Filtering Approach
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Places visited by User
Places visited by Users
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Recommendation Systems: Collaborative Filtering Approach
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I’m Feeling Loco Recommendation System
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User Model User’s Context
Data Mining Techniques
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I’m Feeling Loco Recommendation System
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Recommend Places!
User Model User’s Context Place Model User’s
Mood
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I’m Feeling LoCo System Overview
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Outline
• Details of System’s Components
Recommendation Algorithm:
User Spatiotemporal Constraints Detection
Inference of User Preferences
• Usability Inspection
• Conclusions
• Questions
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Recommendation Algorithm
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Recommendation Algorithm
User’s Transportation Mode delimits places considered.
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Inference of User’s Spatiotemporal
Constraints
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Recommendation Algorithm
User’s Mood delimits places considered.
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Recommendation Algorithm
User’s Mood delimits places considered.
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Serendipidity
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Recommendation Algorithm
Compare each place’s characteristics with the characteristics of the places visited by the user.
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beer, delicious, authentic Mexican, foodies, cash only, Taco Place, Mexican Restaurant, burritos, nachos, college students, partiers, frat boys, taco, hipsters, shopping, clothes, Clothing Store, beer, shopping , food, wine, furniture, Beverage, Food, Furniture, Home goods, Textiles, Grocery or Supermarkets, Design Studio, Furniture or Home Store, Thrift or Vintage Store, Sandwich Place, social stardom
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Automatic Recollection of User Preferences
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Automatic Recollection of Type of Places Visited by User
beer, delicious, authentic Mexican, foodies, cash only, Taco Place, Mexican Restaurant, burritos, nachos, college students, partiers, frat boys, taco, hipsters, shopping, clothes, Clothing Store, beer, shopping , food, wine, furniture, Beverage, Food, Furniture, Home goods, Textiles, Grocery or Supermarkets, Design Studio, Furniture or Home Store, Thrift or Vintage Store, Sandwich Place, social stardom
User Model Text from Places visited by User
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Recommendation Algorithm
Compare each place’s characteristics with the characteristics of the places visited by the user.
hipsters
Log frequency Weight of tag
organic
Log frequency weight score
Log frequency weight score
Log frequency weight score
Log frequency weight score
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beer, delicious, authentic Mexican, foodies, cash only, Taco Place, Mexican Restaurant, burritos, nachos, college students, partiers, frat boys, taco, hipsters, shopping, clothes, Clothing Store, beer, shopping , food, wine,
a b
a+ b a+ b+k+l W+x a+ m+s+iu+l
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Recommendation Algorithm
Recommend the K places with the highest similarity to the places visited by the User
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Overcoming Cold Start Problem
Mine from WikiTravel the landmarks of the city the user is. Use Google places API to obtain the street address of the landmark and distance from the User.
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Usability Inspection of “I’m Feeling LoCo”
•Methodology: Thinking aloud & Cognitive Walkthrough •Users: 8 foursquare users living in two different locations: Portland & Santa Barbara. All had utilized a navigation assistant before. Two had used a personalized travel guide. Most used friends and Yelp for Place suggestion. 22
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Usability Inspection of “I’m Feeling LoCo”
Tasks: •Find a place to eat while walking in downtown Santa Barbara or Portland. •Find a place for celebrating with friends while being a passenger and navigator in a car near Santa Barbara and Portland. •Find a place for studying while biking in Goleta, CA. 23
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Results of the Usability Inspection of “I’m Feeling LoCo”
• Mobile Map
User showed satisfaction with recommendation results.
Difficult to obtain personalized search results in small US towns.
Incremented foursquare usage.
Expose all users to important landmarks.
Improve serendipity.
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Results of the Usability Inspection of “I’m Feeling LoCo”
• Mobile Map Users enjoyed recommendations changing
according to mode of transportation. Need to better map interface offering explicit
routes to destination, specific maps for activities.
Need to offer Eyes Free interaction.
Overall obtained positive reactions from participants.
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Conclusions • Our system provides an outlook on future
developments in personalized LBSs, where the data utilized for generating the recommendations is automatically collected from different information sources.
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