Deep-Sequence–Aware Candidate Generation for e-Learning System

Abstract

Recently proposed recommendation systems based on embedding vector technology allow us to utilize a wide range of information such as user side and item side information to predict user preferences. Since there is a lack of ability to use the sequential information of user history, most recommendation system algorithms fail to predict the user’s preferences more accurately. Therefore, in this study, we developed a novel recommendation system that takes advantage of sequence and heterogeneous information in the candidate-generation process. The principle underlying the proposed recommendation model is that the new sequence based embedding layer in the model catches the sequence pattern of user history. The proposed deep-learning model may improve the prediction accuracy using user data, item data, and sequential information of the user’s profile. Experiments were conducted on datasets of the Korean e-learning platform, and the empirical results confirmed the capability of the proposed approach and its superiority over models that do not use the sequences of the heterogeneous information of users and items for the candidate-generation process.