Recently, the recommendation system has been applied on several domain; movie recommendation is one important and essential application. By the recommendation system, users could quickly have the information about the movies that he/her may be interesting. However, prior studies focused on recommendation system usually suffer from several issues.In this project, we propose a novel intelligent recommendation system based on the combination of deep learning and matrix factorization. The time factor and interesting evolution are considered and included into model construction. We will focus on three topics, (1) We propose an novel prediction model combined the matrix factorization and LSTM model. (2) We use the regression method to effectively describe the preference evolution before utilizing matrix factorization for rating prediction. (3) We propose a bi-LSTM model with the pattern awareness and enhancement. Furthermore, the proposed algorithms and models are applied on real dataset to show the practicability and effectiveness.
|Effective start/end date||1/08/19 → 31/07/20|
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):