Weighted DeepFM: Modeling Multiple Features Interaction for Recommendation System

Recommendation system is an efficient way to filter information, and it has been widely used to predict users' potential interests. To build a powerful recommendation system, we need to use as much information as possible to profile users and items precisely, so it can make more accurate and personalized recommendation for users. The recommender which use merely identifications of user and item as features can't fully utilize the information provided by the data, but also decrease the generalization of the model which have poor performance in both academia and industry. So, using multiple features and learning the intrinsic complex relationship among them are critical for a good recommendation system. In this work, we propose an end-to-end, deep learning based recommendation model without feature engineering. The model consists of two parts, which are used to capture the low-order linear relationship and high-order nonlinear relationship of the features called Weighted Interactive Model and Deep Model. In the Weighted Interactive Model, we use pointwise multiplication to capture low-order relationship of two features. To make the model more expressive, each interaction and dimension of the interactions are assigned by different weights. In addition, we use the user-item rating matrix to build expressions of user and item, which improves generalization of the model. We have conducted many experiments on the open data set for different parameters, and the results outperform than other state-of-the-art methods which show the effectiveness of our proposed model.