Student Research | Personalized Restaurant Recommender System Using A Hybrid Approach

This excerpt is taken from an MSiA student research blog posting. Each month, students in our program submit original extracurricular research as part of our blog competition. The winner(s) are published to the MSiA Student Research Blog, our program website, and receive a chance to attend an analytics conference of their choice. Visit our blog to see more.

Problem Statement

Nowadays, personalized recommendation systems are implemented by many online businesses, which try to identify user’s specific tastes then provide them with relevant products to enhance user’s experience. Users, in turn, would be able to enjoy the convenience of exploring what they potentially like with ease and are often surprised by how good the recommended results are. However, mainstream restaurant recommendation apps have not adopted personalized restaurants recommenders generally. Therefore, finding an ideal restaurant is always a struggle for newcomers and sometimes even for local people, who are looking for places new and exciting to go. Inspired by such a challenge, we aim to build a personalized restaurant recommender system prototype that not only considers the interactions (visits) between customers and restaurants, but also incorporates metadata (side information) that expresses both the customer’s personal tastes and restaurants’ features. Such approach can increase each user’s experience of exploring and finding restaurants that are closer to their tastes. We used datasets provided by Yelp and a package named LightFM, which is a python library for recommendation engines to build our own restaurant recommender.

Here is an introductory article to refresh on some of the basic ideas and jargon on recommender systems before proceeding.

Github repository of LightFM: https://github.com/lyst/lightfm

Github repository for our model: https://github.com/Ivanclj/Yelp-Recommender

Figure 1: Yelp’s home page for a new user in Chicago

Background

When implementing recommender systems, researchers generally adopt two methods: Content-Based Filtering (CBF) and Collaborative Filtering (CF). CBF models users’ tastes based upon their past behaviors, but does not benefit from data on other users. On the contrary, CF looks at user/item interactions (visits) and tries to find similarities among users or items to do the recommender job. However, both methods have their limitations. A CBF-based system is limited by novelty in recommended results. Because it only looks at user’s past history, it never jumps to other areas that users might like but have not interacted with before. Additionally, if the quality of content does not contain enough information to discriminate the items precisely, CBF will perform poorly. On the other hand, CF suffers from sparse interaction matrix between users and items, where many users have very few interactions or even no interactions at all. This is also known as the cold start problem, which would be a huge problem especially for newcomers to certain areas to find suitable restaurants. To tackle this problem, we aim to develop a hybrid approach, which leverages both the collaborative information between users and items and user/item metadata to complete the recommendation job.

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