Inferring intent on mobile devices

[Google CEO Eric] Schmidt said that while the Google Instant predictive search technology helps shave an average of 2 seconds off users’ queries, the next step is “autonomous search.” This means Google will conduct searches for users without them having to manually conduct searches. As an example, Schmidt said he could be walking down the streets of San Francisco and receive information about the places around him on his mobile phone without having to click any buttons. “Think of it as a serendipity engine,” Schmidt said. “Think of it as a new way of thinking about traditional text search where you don’t even have to type.”  – eWeek

When users type phrases into Google, they are searching, but also expressing intent. To create the “serendipity engine” that Eric Schmidt envisions would require a system that infers users’ intentions.

Here are some of the input signals a mobile device could use to infer intent.


Location: It is helpful to break location down into layers, from the most concrete to the most abstract:

1) lat / long – raw GPS coordinates

2) venue – mapping of lat / long coordinates to a venue.

3) venue relationship to user – is the user at home, at a friend’s house, at work, in her home city etc.

4) user movement – locations the user has visited recently.

5) inferred user activity – if the user is at work during a weekday, she is more likely in the midst of work. If she is walking around a shopping district on a Sunday away from her home city, she is more likely to want to buy something. If she is outside, close to home, and going to multiple locations, she is more likely to be running erands.

Weather: during inclement weather user is less likely to want to move far and more likely to prefer indoor activities.

Time of day & date: around mealtimes the user is more likely to be considering what to eat. On weekends the user is more likely to be doing non-work activities. Outside at night, the user is more likely to be looking for bar/club/movie etc.  Time of days also lets you know what venues are open & closed.

News events near the user: they are at the pro sporting event, an accident happened nearby, etc.

Things around the user: knowing not just venues, but activities (soccer game), inventories (Madden 2011 is in stock at BestBuy across the street), events (concert you might like is nearby), etc.

These are just a few of the contextual signals that could be included as input signals.


The more you know about users’ tastes, the better you can infer their intent. It is silly to suggest a great Sushi restaurant to someone who dislikes Sushi. At Hunch we model taste with a giant matrix. One axis is every known user (the system is agnostic about which ID system – it could be Facebook, Twitter, a mobile device, etc), the other axis is things, defined very broadly: product, person, place, activity, tag etc.  In the cells of the matrix are either the known or predicted affinity between the person and thing.  (Hunch’s matrix currently has about 500M people, 700M items, and 50B known affinity points).

Past expressed intent

– App actions:  e.g. user just opened Yelp, so is probably looking for a place to go.

– Past search actions: user’s recent (desktop & mobile) web searches could be indications of later intent.

– Past “saved for later” actions:  user explicitly saved something for later e.g. using Foursquare’s “to do” functionality.

Behavior of other people

– Friends:  The fact that a user’s friends are all gathered nearby might make her want to join them.

– Tastemates: That someone with similar tastes just performed some actions suggests the user is more likely to want to perform the same actions.

– Crowds: The user might prefer to go toward or avoid crowds, depending on mood and taste.

How should an algorithm weight all these signals? It is difficult to imagine this being done effectively anyway except empirically through a feedback loop. So the system suggests some intent, the user gives feedback, and then the system learns by adjusting signal weightings and gets smarter.  With a machine learning system like this it is usually impossible to get to 100% accuracy, so the system would need a “fault tolerant” UI.  For example, pushing suggestions through modal dialogs could get very annoying without 100% accuracy, whereas making suggestions when the user opens an application or through subtle push alerts could be non-annoying and useful.

8 thoughts on “Inferring intent on mobile devices

  1. Pingback: Trackback
  2. Pingback: Trackback
  3. This structured clutch holds elegantly pleated calfskin leather in
    used on to be gold or bronze, getting back together for its overall create.
    Missing Christian Louboutin boots in the party does mean missing probability
    to demonstrate. Our teens are especially vulnerable to free will because
    substantial having to develop up way too fast. It’s a relatively well designed shoe, Ip notes.

  4. At anything in nothing girl’s life, perhaps even now as a grownup,
    there comes a moment when she wishes she could be like Cinderella and wear
    fairly gown. But what about the glass house shoes?
    Well, now’s your for you to win your personal own Christian louboutin shoes Cinderella men’s.

    Here will be the bride. One of these mad. It’s going to never
    be further from me. Nevertheless the bride Christian Louboutin Ariella studded boots, was shopping this week.

    In March, gold-plated one of my favorite online shopping destination the members bring
    a selection of beautiful wedding weekend selection of products in collaboration with Martha Stewart wedding.Louboutin Escarpins

    What presently writing?I also write contemporary romance (for Steeple Hill),
    and the first book around my Lighthouse Lane series (set on Nantucket) was
    released in May possibly Christian Louboutin. I just finished the
    final book.

    Christian Louboutin’s ‘Crazy Fur Glitter Pump’ is a shoe for
    all those who for you to stand out and end up being center of attention. Forward of the
    shoe is often a lavender color and comes accent of shimmering glitter along the crescent toe and heel area.
    The shoe also has a deep purple dyed fine mink fur, as well as attached into the upper back of
    the heel. The mink is dyed in Finland and imported from Italy.
    The fur also protrudes out and complements the adjustable ankle strap,
    very fitting for any foot type and adding comfort. The shoe is lined with a
    soft nude leather, contrasting with it’s signature red singular.
    Match with neon, jewel tone, and pastel apparel. Buy glitter pump
    at Neiman Marcus of your Somerset Collection in Troy, Michigan an internet-based here.

    Matthew 5:27-28 talks about committing adultery in your mind.

    This is called lustful goal. You are not committing the physical act of sex,
    however you are imagining it in mind. This is exactly what Sexting
    is. Lustful thoughts conveyed via word and/or snapshots
    Christian Louboutin.

    To be honest, I got it particularly uncertain about getting handbags and shoes to the lot of
    reasons. One cause was that Being afraid
    of getting ripped off by some replica internet resources and when i almost certainly would
    pay but won’t collect make use of this. It can be performed that For being concerned about that since It’s
    my job to idea that these kinds of internet sites take cash
    transactions or purchases. To calm myself down, I
    started looking for internet sites with excellent reviews.
    An excellent place that i looked for was and what i ran across was an internationally answer
    for fraud.

    People nobody can gain more successes existence only for they have charismatic personality.

    Success will surely come to those who have the right amounts of charisma.
    You have to do is finding out ways can enhance the charisma of yourself
    and be able to dealing with most of instances.

  5. wonderful issues altogether, you just received a brand new reader.

    What could you recommend in regards to your submit that you made some days in the past?
    Any positive?

  6. Pingback: Effective sushi

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s