Improved DeepFM Recommendation Algorithm Incorporating Deep Feature Extraction

Abstract

In recent years, deep learning has been applied to the field of recommendation, which can learn complex user interaction features and make better recommendations. However, deep learning only focuses on the interaction of high-order features and neglects the low-order features. The DeepFM model combines the linear FM (Factorization Machines) model and the deep DNN (Deep Neural Network) model to realize the interactive learning of low-order and high-order features, but it does not take into account that user interests will change dynamically with time. When the data sparsity is high, it cannot be effectively recommended. Based on this, an improved DeepFM recommendation algorithm that combines depth feature extraction was proposed, named fDeepFM. Firstly, the word features are transformed into low-dimensional dense vectors through the Embedding layer. Then Doc2Vec is combined to mine item features with context, and the two are stitched together as the input to the FM model and DNN model. Subsequently, user features are input to the GRU (Gated Cyclic Unit) model according to different cycles to mine user features. Finally, the results of the FM model, DNN model, and GRU model are combined by linear stitching as the overall output of the fDeepFM model. Experiments were carried out on Movielens-20M and Amazon data sets. The experimental results showed that MAE, RMSE, F1-score, and AUC on the Movielens-20M data set were optimized by 1.69%, 2.4%, 1.67%, and 2.28%, respectively; On the Amazon dataset, MAE, RMSE, F1-score, and AUC are optimized by 3.2%, 3.86%, 1.63%, and 2.2% respectively compared with DeepFM.