How to read a user’s mind?Designing algorithms for contextual recommendations

Bharath Mohan CEO, Sensara.tv

Google Now Airport Card

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

Google Now Currency Card

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 country

You’ve never spoken that language

Foursquare Notification

You are near a restaurant

What is known? How is it known?

You like this cuisine

It is lunch time

GPS

Based on past outings

Clock

Sensy Recommendations

You are near 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 WiFiWatch with Vidya

Channels

Your friend likes English Action Based on past views

The Context Engine

What if this super-smart engine guesses exactly what you need, in the context you are in, and gives you something useful?

Understanding context

Reason about a person by modelling his past and future

Analyse context around every dimension - location, meetings, actions, time, etc

Some thumb rules for recommendations

If context is novel, recommend the popular

If context is routine, recommend the novel

If context is novel, 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

If context is routine, 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

Life as a Context Engine

Context engines have to 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.

Context engines have to 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.

Context engines are personal, 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.

Personal assistants are long term 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.