Improving Performance of Recurrent Neural Network based Recommendations by Utilizing Personal Preferences

Dong Shin Lim, Yong Jun Yang, Shin Cho

http://doi.org/10.5626/JOK.2021.48.11.1211

As the amount of content provided on the platform surged, a recommendation system became an essential element of the platform. The collaborative filtering technique is a widely used recommendation system in academia and industry, but it also has a limitation because it relies on quantitative information from consumers such as ratings and purchase history. To overcome this shortcoming, various studies have been done in a bid to improve its performance by collecting qualitative information such as review data in a model. Recently, some studies that applied recurrent neural networks showed better performance than the existing recommendation system by using time-series behavioral data only, but studies that reflect each customer"s preference in the recommendation model have not yet been made. In this paper, an improved recommendation model was presented by calculating a preference matrix based on customer log data and learning it in a recurrent neural network through an embedding vector. It was confirmed that the prediction performance was improved compared to the existing recurrent neural network recommendation model.