Selection of housing, one of the necessities of human life, has a great influence on life for a long time. However, since it requires a wide range of information gathering and consideration before decision, state-of-the-art recommendation algorithms such as collaborative filtering do not work well. In this presentation, after reviewing issues specific to the real estate field, I cited examples of "application of crowdsourcing to social media (Twitter timelines)" and "application of deep learning to property images" as an effort by our research group. Finally I discuss what kind of AI technology is applicable in the real estate field.
Selection of housing, one of the necessities of human life, has a great influence on life for a long time. However, since it requires a wide range of information gathering and consideration before decision, state-of-the-art recommendation algorithms such as collaborative filtering do not work well. In this presentation, after reviewing issues specific to the real estate field, I cited examples of "application of crowdsourcing to social media (Twitter timelines)" and "application of deep learning to property images" as an effort by our research group. Finally I discuss what kind of AI technology is applicable in the real estate field.
Mining User Experience through Crowdsourcing: A Property Search Behavior Corp...Yoji Kiyota
This article describes how to build a property search behavior corpus derived from microblogging timelines, in which tweets related to property search are annotated. We applied microtask-based crowdsourcing to tweet data, and build a corpus which consists of timelines of specific users which are annotated with property search stages (e.g. gathering of property information, and property preview). As a result, property search processes by tens of people were annotated. This corpus is intended to use for redesigning property information services, and marketing information services for potential users.