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  • 1. by
    October 2010
    Chris DeVore, CEO + co-founder
    (206) 801-1080
  • 2. App discovery is a hard problem;Android Market isn’t helping
    “ Discoverability is a problem that has long plagued the world of mobile applications. The issue worsens with each new title added to Apple’s App World and Google’s (not-yet-as-massive) Android Market.
    …the problem of discoverability will only
    grow worse before getting better. ”
    Colin Gibbs, How Carriers Can Crack the App Discoverability Nut, GigaOm, Oct. 9, 2010
  • 3. App discovery engine for Android*Personal, social, on-device
    no user input required
    Personal + Social
    informed by your – and your friends’ – currently installedapps (social data via Facebook Connect)
    statistically generated app-to-app affinities based on install/uninstall data among all participating users
    individual recommendation sets enhanced with fresh AppRank* + social data for maximum relevance
    Always on
    handset app data is polled daily
    recommendations are recalculated several times/day
    background notifications are delivered weekly (or at user-defined intervals)
    *appESPrecommendations engine and methodology are patent-pending IP created by AppStoreHQ. See Appendix for AppRank methodology details.
  • 4. 1
    How does work? (1 of 4)
    Install + opt-in
    Acquire data
    User installs application, opts-in to background (on-device) app discovery and registers at AppStoreHQ
    AppESP polls on-device memory for currently installed applications and passes that data securely to AppStoreHQ servers
  • 5. 4
    How does work? (2 of 4)
    Find patterns
    Statistical relationships among apps are identified via a “collaborative filtering” algorithm (the same approach used by Amazon and Netflix to generate product recommendations)
    We generate individual sets of app recommendations for each user, with a “boost” applied for:
    Apps with high current AppRank score, and
    Apps used by friends (for users who register via Facebook Connect )
  • 6. 5
    How does work? (3 of 4)
    Users are notified of new recommendations via the on-device Notifications shutter
    Recommendations can be tuned via the “Like / Dislike” buttons shown in the app detail view
    Users buy recommended apps directly from Android Market or other approved source
  • 7. 6
    How does work? (4 of 4)
    …with friends
    Facebook is built into appESP’s user-experience and recommendation engine.
    Each user’s social graph is mapped and used to boost app recommendations.
    appESP also shows users what apps their friends have installed and liked.
  • 8. product status
    Production app available now
    Go to or search for “appesp” in Android Market
    Fresh app recommendations are being generated daily based on:
    1B+ app-to-app relationships
    200K+ app-to-content matches
    50K+ individual user profiles
    The appESP recommendations engine is also available to authorized licensing partners via cloud API
    AppStoreHQ is actively seeking distribution partners for the AppESP engine among leading wireless, retail and consumer media players
  • 9. Company Details
    Company: Mobilmeme, Inc.
    Location: Seattle, WA
    Founded: April 2009
    CEO: Chris DeVore,
    CTO: Ian Sefferman,
    Lead Investor: Founders Co-op (Seattle)
  • 10. APPENDIX
    AppRankSM by
  • 11. 2
    How does AppRank work? (1 of 2)
    Continuously index Android Market to maintain a current database of all published apps
    Monitor hundreds of online publishers, plus social streams like Twitter and Facebook, to identify influential reviews and commentary about Android apps

  • 12. 4
    How does AppRank work? (2 of 2)
    Follow every link in discovered content – including shortened URLs and redirects – to match app mentions to published apps
    Several matching approaches are used, including:
    • Android package name
    • 13. Developer website URL
    • 14. AppBack widgets
    • 15. Manual validation
    Several times a day, force-rank all listed applications based on an algorithm that takes into account:
    • The number of discovered
    mentions for each app
    • The relative authority of each
    mention (using both 3rd-party
    sources and internal quality
    scoring methods)
    • The recency of each mention
    (adding decay so older mentions
    matter less than new ones)