Adapting session based recommendation for features through transfer learning
Even Oldridge · 2018
This industry talk covers the deep learning architecture developed at Realtor.com to recommend real estate listings to our userbase. The recommendation of homes is a different problem than most other domains both in the sense that listings are unique and that there are additional geographic and time constraints that increase the sparsity of interactions and make recommendation of individual listings more challenging. In particular time on market in a hot area can be limited to weeks or even days, and listing cold-start is critical to providing up to date market information. Thankfully the structured feature data for listings is incredibly rich and provides a framework from which to map listings into a meaningful vector space. User first impressions are also incredibly important in this highly competitive field, and offline recommendation or models that don't adapt during the users session are less desirable.