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Creating PebbleAppTailor
for
Maureen Teyssier, Insight Fellow
without
apps
with
apps
user
action
Increasing User Engagement
Pebble doesn’t have App rating data!
database of usage
Pebble doesn’t have App rating data!
database of usage
Metric for Engagement:
Pebble doesn’t have App rating data!
Launch count per App per User
Predict usage of all Apps for a User,
based on how they use the Apps they already have
Recommend the Apps that will be used the most.
Recommend the Apps that will be used the most.
Predict usage of all Apps for a User,
based on how they use the Apps they already have
• Launch Count as Proxy for User Ratings
• Item-based Collaborative Filtering
• Cosine-Similarity Matrix
• Sparsity of Dataset: 0.6%
• Sparsity of Recommendation Matrix: 2.5%
Recommend the Apps that will be used the most.
Predict usage of all Apps for a User,
based on how they use the Apps they already have
Pebble Database
PebbleAppTailor
Leave-One-Out Validation
User’s Apps = [PebbMine, Cards, YachtTimer, …….]
Leave-One-Out Validation
User’s Apps = [PebbMine, Cards, YachtTimer, …….]
Can I recommend the App I removed?
Leave-One-Out Validation
User’s Apps = [PebbMine, Cards, YachtTimer, …….]
Can I recommend the App I removed?
Next Steps
• Categorize and subcategorize Apps (NLP)
• Select/Exclude App categories
• Highlight App categories that emphasize
uniqueness of Pebble Hardware
Built a recommender:
• emphasizes Apps with high user engagement
• works really well
• will be a tool that Pebble uses going forward
Maureen Teyssier
Maureen Teyssier
Creating PebbleAppTailor
for
Maureen Teyssier, Insight Fellow
This user loves games.…
This user loves games, and is recommended games.
s user cares about weather, directions, GPS. Lives in Netherlands
s for the Netherlands, 3 different types of GPS/Map Apps, a func

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