MENN: A Hybrid Model for User Preference Mining

User preference mining uses rating data, item content or comments to learn additional knowledge to support the prediction task. For the use of rating data, the usual approach is to take rating matrix as data source, and collaborative fil-tering as the algorithm to predict user preferences. Item content and comments are usually used in sentiment analysis or as auxiliary information for other al-gorithms. However, factors such as data sparsity, category diversity, and nu-merical processing requirements for aspect sentiment analysis affect model performance. This paper proposes a hybrid method, which uses the deep neural network as the basic structure, considers the complementarity of text and nu-meric data, and integrates the numeric and text embedding into the model. In the construction of text-based embedding, extracts the text summary of each text-based review, and uses the Doc2vec to convert the text summary into mul-ti-dimensional vector. Experiments on two Amazon product datasets show that the proposed model consistently outperforms other baseline models, achieving an average reduction of 15.72% in RMSE, 24.13% in MAE, and 28.91% in MSE. These results confirm the effectiveness of our proposed method for learning user preferences.