Abstract: Addressing the current challenges in recommendation systems, which include difficulties in obtaining feature information and the lack of effective methods to represent the weights of feature information, this study proposes a recommendation model based on the attention mechanism and feature fusion, named FFADeepCF_SPS. Firstly, to address the inadequate feature representation, the Factorization Machines (FM) are employed to fuse features, extending them from one-dimensional to high- dimensional space to obtain low-order feature representations. Subsequently, a Deep Neural Network (DNN) is used to learn high-order features, and the two types of features are combined through a fully connected layer to obtain the required feature representation. Secondly, to address the issue of excessive weight skewing in the single-head attention mechanism, a multi-head attention mechanism is adopted, where the input is divided into multiple single heads to calculate their attention weights separately. The results from each head are then concatenated through a linear transformation to obtain the final output. Finally, combining the above two points, a recommendation model based on the attention mechanism and feature fusion is constructed. To validate the effectiveness of the model, comparative experiments and ablation studies are conducted on four public datasets against baseline models such as GMF, DeepCF_SPS, and CNN-BiLSTM. The experimental results show that the proposed model outperforms the baseline models in terms of MSE, RMSE, and MAE evaluation metrics across datasets of different sizes.

Key words: attention mechanism, feature fusion, recommendation model, score prediction