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
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This document describes a study that aimed to establish a method for understanding user experiences in property searching through analyzing Twitter timelines. The researchers collected Twitter timelines of followers of a Japanese property search service account and used crowdsourcing microtasks to extract tweets related to property searching and analyze them based on a conventional property search process framework. Workers were asked to categorize timeline fragments as either related or unrelated to property searching. This allowed the researchers to build a corpus of property search behavior data derived from social media for analyzing user needs and experiences.