- 學位論文
一個結合矩陣分解與長短期記憶模型的動態推薦系統
A Hybrid Dynamic Recommendation System based on Matrix Factorization and Long Short-Term Memory (LSTM)
摘要
由於對用戶興趣以及嗜好的精確預測，矩陣分解（matrix factorization，MF）技術已被廣泛應用於推薦系統中。先前基於矩陣分解的方法通過從使用者（user）和項目（item）中提取潛在因子（latent factor）來調整總體評級以進行推薦。然而，在實際應用中，人們的偏好通常會隨著時間的推進而發生改變，傳統基於矩陣分解的方法已經無法正確地捕捉用戶和興趣之間的變化。在這篇論文當中，通過將遞歸神經網絡（recurrent neural network，RNN）結合到矩陣分解中，我們開發了一種新穎的推薦系統M-RNN-F，以有效地描述用戶隨時間的偏好演變，提出了兩種學習模型來捕捉演化模式並預測未來的用戶偏好。實驗結果顯示，M-RNN-F的性能優於其他最先進的推薦演算法。此外，我們在現實世界數據集上進行實驗，以證明其實用性。
並列摘要
Matrix factorization (MF) technique has been widely utilized in recommendation systems due to the precise prediction of users’ interests. Prior MF-based methods adapt the overall rating to make the recommendation by extracting latent factors from users and items. However, in real applications, people’s preferences usually vary with time; the traditional MF-based methods could not properly capture the change of users’ interests. In this thesis, by incorporating the recurrent neural network (RNN) into MF, we developed a novel recommendation system, M-RNN-F, to effectively describe the preference evolution of users over time. Two learning models are proposed to capture the evolution pattern and predict the user preference in the future. The experimental results show that M-RNN-F performs better than other state-of-the-art recommendation algorithms. In addition, we conduct the experiments on real world dataset to demonstrate the practicability.