Inferring Restaurant Styles by Mining Crowd Sourced Photos from User-Review Websites

TL;DR

This paper introduces a novel deep learning framework that infers restaurant styles from user-uploaded photos on review websites, enabling more intuitive restaurant searches based on ambiance and dish styles.

Contribution

It presents a new dataset linking photos to restaurant styles and develops a multi-instance multi-label learning approach with a two-step CNN training process for style inference.

Findings

Effective restaurant style inference with sufficient photos

Deep multi-label CNN outperforms baseline methods

Framework applicable to crowd-sourced restaurant data

Abstract

When looking for a restaurant online, user uploaded photos often give people an immediate and tangible impression about a restaurant. Due to their informativeness, such user contributed photos are leveraged by restaurant review websites to provide their users an intuitive and effective search experience. In this paper, we present a novel approach to inferring restaurant types or styles (ambiance, dish styles, suitability for different occasions) from user uploaded photos on user-review websites. To that end, we first collect a novel restaurant photo dataset associating the user contributed photos with the restaurant styles from TripAdvior. We then propose a deep multi-instance multi-label learning (MIML) framework to deal with the unique problem setting of the restaurant style classification task. We employ a two-step bootstrap strategy to train a multi-label convolutional neural network (CNN). The multi-label CNN is then used to compute the confidence scores of restaurant styles for all the images associated with a restaurant. The computed confidence scores are further used to train a final binary classifier for each restaurant style tag. Upon training, the styles of a restaurant can be profiled by analyzing restaurant photos with the trained multi-label CNN and SVM models. Experimental evaluation has demonstrated that our crowd sourcing-based approach can effectively infer the restaurant style when there are a sufficient number of user uploaded photos for a given restaurant.