A Joint Learning Framework for Restaurant Survival Prediction and Explanation (2022)

Abstract: The bloom of the Internet and the recent breakthroughs in deep learning techniques open a new door to AI for E-commence, with a trend of evolving from using a few financial factors such as liquidity and profitability to using more advanced AI techniques to process complex and multi-modal data. In this paper, we tackle the practical problem of restaurant survival prediction. We argue that traditional methods ignore two essential respects, which are very helpful for the task: 1) modeling customer reviews and 2) jointly considering status prediction and result explanation. Thus, we propose a novel joint learning framework for explainable restaurant survival prediction based on the multi-modal data of user-restaurant interactions and users’ textual reviews. Moreover, we design a graph neural network to capture the high-order interactions and design a co-attention mechanism to capture the most informative and meaningful signal from noisy textual reviews. Our results on two datasets show a significant and consistent improvement over the SOTA techniques (average 6.8% improvement in prediction and 45.3% improvement in explanation).

## introduction business survival prediction is a hot topic in management and finance literature. traditional methods rely heavily on financial factors to research(e.g., liquidity, solvency, and profitability) @xcite @xcite . however, there are two significant drawbacks: 1) the financial factors of a shop/company are hard to obtain due to privacy issues; 2) meanwhile, financial factors are macro indicators that reveal the status of a business only on a coarse level. with the development of information techniques, much restaurant-related data can be collected online. for example, people can post check-ins after consuming in a restaurant, and they can share reviews to show how/why they like the restaurant via an online review platform, such as yelp.com. moreover, reviews contain informative users' feedback on a fine-grain level. more importantly, the feedback which deeply reflects the restaurant's operating status, can in turn help to generate explainable prediction reasons. some recent research works also verify this, and the use of online reviews to understand business performance is an emerging trend @xcite . recent advances in deep learning have various models that research reviews and interactions for different kinds of tasks, such as recommendation @xcite , fake news detection @xcite , rating prediction @xcite , but little attention has been paid to the application of restaurant survival analysis. in this paper, we propose a novel joint learning framework to tackle the challenging task of explainable restaurant survival prediction. our model consists of two compulsory modules: the co-attention network for selecting valuable review texts and the graph neural network for learning high-order interactions on the user-restaurant graph. specifically, the co-attention mechanism is used to select meaningful review text, which is a feature selection and learning process. the graph of user-item interactions could reveal the preference similarity between users (or items). therefore, the construction of graph neural networks, on which we encode high-order relationships, can en-hance the representation of reputed users and high-quality restaurants by modeling the high-order interaction between user and restaurant, which is the key to our modeling exemplification. merely predicting the future status of restaurant survival is inadequate. it is also critical for businesses to understand why they will prosper or close in the future. fortunately, we can leverage nlp models to encode the massive user reviews and output some explanations, just like a document summarization process. to this end, we jointly train the survival prediction and explanation task. the prediction task predicts the future status of the restaurant, and the explanation task generates some explainable texts to provide an informative summarization for the restaurant's management. we named the unified framework restaurant survival prediction and explanation (rspe). through experiments, we find that rspe significantly improves the performance of both tasks compared with several competitive baselines. the main contributions of our framework are as follows. ## related work restaurant survival analysis: store survival analysis is an essential and practical research topic in the financial and marketing field, which offers deep insights into stores' financial affairs, marketing strategies, and management @xcite @xcite . traditionally, researchers usually leverage restaurant financial factors to build linear forecasting models, which are sensitive and hard to obtain. with the development of online services, researchers find that user generated content (ugc), such as textual reviews from yelp.com or dianping.com, contains massive information covering diverse aspects of stores (restaurants in this paper). leveraging the heterogeneous ugc can effectively improve the performance of restaurant survival prediction models @xcite . however, the main weaknesses of this group of methods are tthreefold: 1) they used traditional nlp models such as lda, bag-of-words, or word2vec; 2) they did not consider the interaction graph between customers and restaurants; 3) they did not explicitly reduce the noisy information from the raw ugc. ## problem statement we first introduce some definitions and notations, then introduce the problem formulation. user-restaurant interaction graph: let @xmath0, v, e) represent the user-restaurant interaction graph, where @xmath1, u 2 ..., u n } denotes the set of users, and @xmath2, v 2 ..., v m } is a set of restaurants. @xmath3, v)|@xmath4, @xmath5, where an edge (u, v) ∈ e means that user u has reviewed restaurant v. reviews: the reviews of a restaurant are defined as (r prediction task: to predict the future status of the restaurant. this is a binary classification task, and 0 means the restaurant will be shut down and 1 means normal operation. explanation task: besides the binary prediction task, the model also contains an explanation task, in which a sentence of summarization text @xmath6, . . . , w t ) will be generated. we invited 30 evaluators who are split into two groups to manually select a few sentences (about 30 words) from all the restaurant reviews to represent the key reasons for each restaurant's business prosperity, which will be used as ground-truth for training and evaluation. problem formulation: with the interaction graph g and review collections r (u ) and r (v ) as input data, we want to make predictions for a given restaurant regarding its future status and meanwhile generate an explaining text. ## the proposed model in this section, we introduce our model, a joint learning framework for restaurant survival prediction and explanation, which is illustrated in figure1. there are four components in rspe, including an input module, a co-attention module, a graph representation module and a joint learning module. we will introduce the details in the following sections. ## conclusion in this paper, we tackle the problem of restaurant survival, which is an essential task for social good. unlike traditional methods, which highly rely on sensitive financial indicators, we use deep learning techniques to mine useful signals from massive ugc. we are the first to conduct both future status prediction and explanation simultaneously as a joint framework. our model has two key components, i.e., the graph representation module and the co-attention module. we conduct extensive experiments on two datasets. results demonstrate that our proposed model achieves the sota performance on both prediction and explanation tasks.