App discovery is a hard problem;Android Market isn’t helping<br />“ 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.<br />…the problem of discoverability will only<br />grow worse before getting better. ”<br />Colin Gibbs, How Carriers Can Crack the App Discoverability Nut, GigaOm, Oct. 9, 2010<br />2<br />
App discovery engine for Android*Personal, social, on-device<br />Automatic <br />no user input required<br />Personal + Social<br />informed by your – and your friends’ – currently installedapps (social data via Facebook Connect)<br />Relevant<br />statistically generated app-to-app affinities based on install/uninstall data among all participating users<br />individual recommendation sets enhanced with fresh AppRank* + social data for maximum relevance<br />Always on<br />handset app data is polled daily <br />recommendations are recalculated several times/day<br />background notifications are delivered weekly (or at user-defined intervals)<br />*appESPrecommendations engine and methodology are patent-pending IP created by AppStoreHQ. See Appendix for AppRank methodology details. <br />3<br />
1<br />How does work? (1 of 4)<br />2<br />Install + opt-in<br />Acquire data<br />User installs application, opts-in to background (on-device) app discovery and registers at AppStoreHQ<br />AppESP polls on-device memory for currently installed applications and passes that data securely to AppStoreHQ servers<br />4<br />
4<br />3<br />How does work? (2 of 4)<br />Find patterns<br /> Recommend<br />Statistical relationships among apps are identified via a “collaborative filtering” algorithm (the same approach used by Amazon and Netflix to generate product recommendations)<br />We generate individual sets of app recommendations for each user, with a “boost” applied for:<br />Apps with high current AppRank score, and<br />Apps used by friends (for users who register via Facebook Connect )<br />5<br />
5<br />How does work? (3 of 4)<br />Discover…<br />Users are notified of new recommendations via the on-device Notifications shutter<br />Recommendations can be tuned via the “Like / Dislike” buttons shown in the app detail view<br />Users buy recommended apps directly from Android Market or other approved source<br />6<br />
6<br />How does work? (4 of 4)<br />…with friends<br />Facebook is built into appESP’s user-experience and recommendation engine.<br />Each user’s social graph is mapped and used to boost app recommendations. <br />appESP also shows users what apps their friends have installed and liked. <br />7<br />
product status<br />Production app available now<br />Go to http://www.appesp.com or search for “appesp” in Android Market<br />Fresh app recommendations are being generated daily based on:<br />1B+ app-to-app relationships<br />200K+ app-to-content matches<br />50K+ individual user profiles<br />The appESP recommendations engine is also available to authorized licensing partners via cloud API<br />AppStoreHQ is actively seeking distribution partners for the AppESP engine among leading wireless, retail and consumer media players<br />8<br />
Company Details<br />Company: Mobilmeme, Inc.<br />Location: Seattle, WA<br />Founded: April 2009<br />CEO: Chris DeVore, email@example.com<br />CTO: Ian Sefferman, firstname.lastname@example.org<br />Lead Investor: Founders Co-op (Seattle)<br />9<br />
2<br />1<br />How does AppRank work? (1 of 2)<br />Continuously index Android Market to maintain a current database of all published apps<br />Monitor hundreds of online publishers, plus social streams like Twitter and Facebook, to identify influential reviews and commentary about Android apps<br />…<br />…<br />11<br />
4<br />3<br />How does AppRank work? (2 of 2)<br />Follow every link in discovered content – including shortened URLs and redirects – to match app mentions to published apps<br />Several matching approaches are used, including:<br /><ul><li>Android package name
Manual validation </li></ul>Several times a day, force-rank all listed applications based on an algorithm that takes into account:<br /><ul><li>The number of discovered </li></ul> mentions for each app<br /><ul><li> The relative authority of each </li></ul> mention (using both 3rd-party <br /> sources and internal quality <br /> scoring methods)<br /><ul><li> The recency of each mention </li></ul> (adding decay so older mentions <br /> matter less than new ones)<br />12<br />
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