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Interpretable recommender system with deep learning and NLP using social media data


Term:

Summer

Department:

MAS: Media Arts and Sciences

Faculty Supervisor:

Kent Larson

Faculty email:

kll@mit.edu

Apply by:

June

Contact:

Luis Alonso: alonsolp@mit.edu

Project Description

Many real-world data cover reviews and ratings on business, creating opportunities for marketing companies to better understand the demand and preferences of customers with complementary information. However, to effectively combine data with multi-modal nature and complex structure is challenging. Users post reviews on the businesses, revealing the characteristics of businesses they care. We aim to use users’ reviews as auxiliary information and the reviews to build personalized recommendations. We will develop a interpretable deep learning framework to utilize nodal feature information and graph structures effectively. Our framework will extract the characteristics of the business and users, which will enable us to understand what do users care about and how do businesses perform in different dimensions. The method will be evaluated on the scraped data via TripAdvisor, to demonstrate its effectiveness.

Pre-requisites

Skills you need: — Proficiency in Python (Required) — Machine learning (Required) — Experience in data scraping (Bonus) — Natural language processing (Optional) — Deep learning (Optional) — Social networks (Optional) Skills you will learn: — Graph-based deep learning — Behavioral data analysis We have a collaboration with Andorra, Germany, and the Mexican government. You will have an excellent opportunity for both publishing academic papers and make a real change!