Deep Learning Ar chitectur e for Choice-based Recommendation System: A Case Study of Flight Sear ch Engine

Abstract

First, we propose a class of efficient models classed as choice-based recommendation (CBR) for parametric metrics, such as a logit model as a recommendation system using nonparametric approaches. The rest of the papers is organized as follow : we used a simple, streamlined architecture that uses a nonparametric approach such as a feedforward deep neural network (DNN). The study implemented a method to deal with a choice set with a fixed and variable-length option, investigate deep learning methods that consider each choice set as one sample point, the effect of embedding categorical features and accuracy impact, and the efficiency of batch normalization toward a more stable network. To check the performance of our approach, we conducted extensive experiments on multiple datasets and used the top-k accuracy as a metric. We then show the effectiveness of CBR across two industrial applications and use cases, including hotel booking and airline itineraries. The results show that the DNN outperforms the multinomial logit model (MNL) with significant top-k accuracy. The top-k accuracy was further divided into three different DNN models. Among the models, a model that included a layer with batch normalization embedding outperforms with top-k accuracy compared with the model that does not include both batch normalization and embedding layer in the proposed DNN architecture.