PebbleAppTailor

1. Creating PebbleAppTailor
for
Maureen Teyssier, Insight Fellow

2. without
apps
with
apps
user
action
Increasing User Engagement

3. Pebble doesn’t have App rating data!

4. database of usage
Pebble doesn’t have App rating data!

5. database of usage
Metric for Engagement:
Pebble doesn’t have App rating data!
Launch count per App per User

6. 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.

7. 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

8. • 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

9. Pebble Database
PebbleAppTailor

10. Leave-One-Out Validation
User’s Apps = [PebbMine, Cards, YachtTimer, …….]

11. Leave-One-Out Validation
User’s Apps = [PebbMine, Cards, YachtTimer, …….]
Can I recommend the App I removed?

12. Leave-One-Out Validation
User’s Apps = [PebbMine, Cards, YachtTimer, …….]
Can I recommend the App I removed?

13. Next Steps
• Categorize and subcategorize Apps (NLP)
• Select/Exclude App categories
• Highlight App categories that emphasize
uniqueness of Pebble Hardware

14. Built a recommender:
• emphasizes Apps with high user engagement
• works really well
• will be a tool that Pebble uses going forward

15. Maureen Teyssier

16. Maureen Teyssier

17. Creating PebbleAppTailor
for
Maureen Teyssier, Insight Fellow

19. This user loves games.…

20. This user loves games, and is recommended games.

21. s user cares about weather, directions, GPS. Lives in Netherlands

22. s for the Netherlands, 3 different types of GPS/Map Apps, a func