The Decade of the Data Translator The Decade of the Data Translator

Recommendation Systems

Friday, April 16, 2021

- EDT
Introduction to TigerGraph Cloud - How to Run TigerGraph on AWS, Azure and GCP
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Emma Liu
Emma Liu
TigeGraph, Senior Product Manager

TigerGraph Cloud, a cloud agnostic database-as-a-service offering, enables users to leverage graph analytics on AWS, Azure, and GCP. TigerGraph Cloud not only fully manages clusters, but also provides a use case library via starter kits, which greatly reduces customers’ time to business value and decreases the learning curve to specific graph database use cases such as fraud detection, recommendation, customer 360, AI and in-database machine learning. This session provides an end-to-end overview of TigerGraph Cloud with a short demonstration that showcases starter kits and other unique functionality.

- EDT
A Home Recommender System to Promote Path to Homeownership
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Anshul Agarwal, PhD
Anshul Agarwal, PhD
Home Partners of America, Data Science Director

Home Partners of America (HPA) helps families access high-quality single-family rental homes in desirable communities and provides them with a potential path to homeownership through its unique resident-led Lease Purchase (LP) Program. HPA acquires single-family homes exclusively through this program which are chosen by applicants themselves from market listings, and leases are executed to rent. Promoting awareness of such a program to renters can bring them closer and sooner to owning their dream home. In this talk we describe a unique data-driven home recommender system (HRS) to expand the reach of the LP program. Based strictly on a user’s home preferences and desired features in a home, the HRS not only recommends potential rental homes to a renter, but also introduces the LP program while recommending active listings they could consider renting and owning. The model particularly ensures fair housing laws are followed and there’s no geographic or other kinds of discrimination in recommendations. 

In this talk, we describe the technical details of the model as well as design considerations specific to this problem. Because of the nature of user-home interactions, the HRS is built on a novel unique mixed-integer linear programming (MILP) framework. The talk will cover why traditional algorithms such as collaborative filtering or matrix factorization are not pertinent, and how the MILP framework addressed the needs appropriately.