AI Case Study

"Trulia, the residential real estate site, started the journey early: More than four years ago, it started building its own AI platform with the goal of creating a more personalized, predictive experience for its users. So far, Trulia says the effort has paid off with a double-digit increase in consumer engagement.

Given its specific needs as a residential real estate site, the company invested more in building its object detection system. "We can look into a kitchen and say this is a kitchen -- a kitchen with white granite. Or we can look into the living room and says it has hardwood floors."

Trulia's computer vision technology also looks for what it calls the most "attractive" photos. "We know with our own experiences of home buying, when we start the search... and start looking at the photos, if those are not engaging, you will go to the next photo, or the next listing," Varma said.

A photo's "attractiveness" is calculated with three variables: whether it is an appropriate image, the quality of the image and the relevancy. An image of a backyard with a pool would be dubbed "appropriate" if, for instance, a user was searching for homes with pools. However, it would not be a "relevant" image if it were simply the image used on a real estate agent's business card, for example.

Along with these enhanced visual products, Trulia has invested in a "recommender" system that aims to introduce users to properties that may appeal to them, even if they fall outside of their specific, stated preferences. That system is based, in part, on a "collaborative filtering" technique.

To round out its AI-powered tools, Trulia built a consumer prediction engagement model. "When we send you content, we look at what content you engage with and what you are not engaged with," Varma said, "and we only send you what you want rather than overwhelming you in this journey and sending you emails and push notifications that are not relevant."

For computer vision, the company has trained models with open source frameworks like Caffe while exploring support for TensorFlow. On top of that, it's invested in its own servers with GPUs. They're using languages like Python to write some machine learning models, while Scala and Java are mostly used on the serving side."