Recommender systems are essential information technologies today, and recommendation algorithms combined with deep learning have become a research hotspot in this field. The recommendation model known as LFM (Latent Factor Model), which captures latent features through matrix factorization and gradient descent to fit user preferences, has given rise to various recommendation algorithms that bring new improvements in recommendation accuracy. However, collaborative filtering recommendation models based on LFM lack flexibility and has shortcomings for real-time recommendations, as they need to redo the matrix factorization and retrain using gradient descent when new users arrive. In response to this, this paper innovatively proposes a Latent Factor Generator (LFG) network, and set the movie recommendation as research theme. The LFG dynamically generates user latent factors through deep neural networks without the need for re-factorization or retrain. Experimental results indicate that the LFG recommendation model outperforms traditional matrix factorization algorithms in recommendation accuracy, providing an effective solution to the challenges of real-time recommendations with LFM.

翻译：推荐系统是当今重要的信息技术，结合深度学习的推荐算法已成为该领域的研究热点。基于潜在因子模型（LFM）的推荐模型通过矩阵分解和梯度下降捕捉潜在特征以拟合用户偏好，由此衍生出的多种推荐算法在推荐准确性上不断取得新提升。然而，基于LFM的协同过滤推荐模型缺乏灵活性，且在实时推荐方面存在缺陷——当新用户出现时需重新执行矩阵分解并利用梯度下降进行重新训练。为此，本文创新性地提出一种潜在因子生成器（LFG）网络，并以电影推荐为研究主题。LFG通过深度神经网络动态生成用户潜在因子，无需重新分解或重新训练。实验结果表明，LFG推荐模型在推荐准确性上优于传统矩阵分解算法，为LFM在实时推荐中面临的挑战提供了有效解决方案。