Post-filtering for a restaurant context-aware recommender system

Abstract

Nowadays recommender systems are successfully used in various fields. One application is the recommendation of restaurants, where even if the method of customer service is the same, the quality of service varies depending on the resources invested to improve it. Traditionally, in a restaurant a waiter takes orders from customers and then delivers the product. The motivation of this work is to make recommendations of restaurants with the aim of disseminating information about products and services offered by restaurants in the city of Tijuana through a Web based platform. The proposed recommendation algorithm is based on contextual post-filtering approach, using the output of a collaborative filtering algorithm together with contextual information of the user’s current situation. The dataset used was explicitly acquired through questionnaires answered by 50 users; and the experiment was performed with a data set of 1,422 ratings of 50 users and 40 restaurants. We evaluate our approach with Mean Absolute Error (MAE) using dataset obtained of the questionnaire and the experimental results show that our approach has an acceptable accuracy for the dataset used.