|
Restaurant recommendation systems are able to alleviate the information overload problem substantially in the catering industry and facilitate customers to make decisions. Building an effective restaurant recommendation system can bring considerable profits and improve user experience. In this paper, we propose a neural restaurant recommendation model with heterogeneous attention (Restaurant Heterogeneous Attention Model). We use heterogeneous features that users may use as references to construct both restaurant representation and user representation of our model. In our model, the main approach to obtain restaurant and user representation is a restaurant encoder and a user encoder. In the restaurant encoder, we propose a heterogeneous attention model to learn unified restaurant representation. We consider the restaurant name, longitude, latitude, and categories as the encoder input and use both word-level attention and feature-level attention to assist the model to attend to significant words and features. In the user encoder, we learn the user representation based on the restaurants that the user has been to. Also, the different restaurants that the user has been to will provide different information for user encoder learning. In order to find which restaurant is more informative for user representation learning, we also apply an attention mechanism to build our user encoder. After conducting many experiments with a dataset from Yelp, the result shows our model can greatly improve the performance of restaurant recommendations.
|
