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Uploaded byBharath Kumar Mohan
How to read a user's mind
The human mind is going through thousands of thoughts everyday. A perfect recommender system needs to know what is going on and suggest something useful - at all times, without being perceived as intrusive or noisy. After slicing every possible sensor within the reach of a digital system - from the GPS, Accelerometer, Time of day, Temperature, Browsing History, TV Viewing, Sound, a "perfect recommender system" should learn which ones to give more importance to, predict the state of mind - in order to make the most effective recommendations. This talk takes an exemplary approach to derive some heuristics that can drive algorithms.
How to read a user's mind
- 1. How to reada user’s mind? Designing algorithms for contextual recommendations Bharath Mohan CEO, Sensara.tv
- 2. Google Now AirportCard You are taking a flight What is known? How is it known? The flight starts at 6:45 PM Airport is 45 minutes away Your email Flight database GPS + Nav + Traffic
- 3. Google Now CurrencyCard You are in an airport What is known? How is it known? A foreign country A country that speaks a different language GPS Different from your home You’ve never spoken that language
- 4. Foursquare Notification You arenear a restaurant What is known? How is it known? You like this cuisine It is lunch time GPS Based on past outings Clock
- 5. Sensy Recommendations You arenear a TV What is known? How is it known? You like English Action You have someone with you Same WiFi as TV Based on past views Her phone in same WiFi Watch with Vidya Channels Your friend likes English Action Based on past views
- 6. The Context Engine Whatif this super-smart engine guesses exactly what you need, in the context you are in, and gives you something useful?
- 7. Understanding context Reason abouta person by modelling his past and future Analyse context around every dimension - location, meetings, actions, time, etc
- 8. Some thumb rulesfor recommendations If context is novel, recommend the popular If context is routine, recommend the novel
- 9. If context isnovel, recommend the popular At 1PM, a near-by and popular vegetarian restaurant in Mumbai normally eats actionable out of place - home in Bangalore best recommendation generally visits vegetarian
- 10. If context isroutine, recommend the novel At home at 8 PM, an action movie that’s airing for the first time on TV normally watches TV generally watches action most novel
- 11. Life as aContext Engine
- 12. Context engines haveto be there with you, when you are doing things. #1 The curse of any recommender system is that a user never asks for one. Context engines have to neatly fold into the experience of something you are doing already.
- 13. Context engines haveto be interesting and precise. #2 Do you recall Clippy? That annoying personal assistant on MS Office, that’d popup and tell you the obvious. Context engines cannot make errors. Even a right thing, if told at a wrong time is annoying.
- 14. Context engines arepersonal, and should grow closer. #3 I may love Mediterranean food, but only during lunch time. You may love Greek, but only on weekends. A personal assistant that gathers your trust, must grow on it – on continued usage. It should offer explanations, ask for feedback and constantly learn and react.
- 15. Personal assistants are longterm companions. #4 Its like marriage. Personal assistants have to find that sweet spot where users will continue to have them even after the honeymoon phase.