# 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 Doc2 Vec 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 Movielers-20M and Amazon data sets. The experimental results showed that _MAE_, _RMSE_, _F1-score_, and _AUC_ on the Movielers-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. feature extraction; deep learning; DeepFM; vector representation; GRU +
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are prone to the problem of combinatorial explosion. Hence [PERSON] et al. added deep neural networks to the _FM_ model to improve the expressiveness of the model and proposed the NFM model [5]. [PERSON] et al. proposed the HoAFN model [6] because the AFM model cannot extract higher-order features. However, all the above methods are linear improvements based on the _FM_ model, and the model results are still unsatisfactory. The PNN model [7] was proposed by the team at Shanghai Jiao Tong University at the ICDM conference. It can obtain the cross information between features in a more targeted way. [PERSON] et al. proposed the classical Wide & Deep model [8], which combines linear models with deep learning, but Wide partially relies on manual feature screening and cannot extract high-order and low-order features simultaneously. [PERSON] et al. proposed the DeepFM model [9] based on the Wide & Deep model, which combines high-order and low-order features while alleviating the problem of insufficient low-order feature representation and easy overfitting of high-order features. It has attracted people's attention. [PERSON] et al. combined the advantages and features of the DeepFM model based on the FwFm model, considered the changes in the external environment and internal perception, and constructed the FG_DRFwFm model for learning the interaction of high- and low-order features of multiple features. Experiments showed better recommendation results [10]. [PERSON] et al. introduced the attention mechanism into the DeepFM model and proposed the DIFMN model, which can better consider the diversity of user interests [11]. Inspired by the DeepFM model, [PERSON] et al. proposed a DCFM model by combining Factorization Machine crossover networks and deep neural networks, combining user and commodity characteristics in many aspects [12]. [PERSON] et al. improved the parameters of the DeepFM model and combined the sensors of loMT to form a healthcare system for disease prediction, which performed better in terms of accuracy and time [13]. The time factor is often an important factor in influencing the user's choice, and the items preferred in the same period are likely to be the same type of items.