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