Feature-Over-Field Interaction Factorization Machine for Sparse Contextualized Prediction in Recommender Systems

With the continuous growth and development of Web information, the prediction of users’ sparse behavior has become a research hotspot in recommender systems. Recently, factorization machine (FM) is proposed to alleviate the problem of inaccurate prediction accuracy to a certain extent in sparse datasets. The main idea of FM is to capture rich semantic relations with second-order feature interactions. Subsequently, inspired by feature interactions of FM, interaction-aware factorization machine (IFM) introduces the concept of field interaction to obtain more accurate predictions, and its primary motivation is combining feature interactions with field interactions to expand the potential interaction characteristics. Based on IFM, we propose a feature-over-field interaction factorization machine (FIFM), which is constructed on the basis of feature interactions and field interactions, and design a feature-over-field interaction mechanism (FIM) to exploit the effectively predictive signals hidden in the interaction context. Then, fusing interactive-aware method is adapted to predict users’ behaviors in different sparse scenarios. Besides, we propose a neural network version based on deep learning named generalized feature-field interaction model (GFIM) to further extract more nonlinear higher-order interaction signals, which consumes more parameters as well as has higher time complexity, and could be used in the high computational scenarios. Extensive experiments on four real-world datasets show that our proposed approaches FIFM and GFIM outperform the state-of-the-art method IFM in the metric of RMSE. Moreover, we conduct comprehensive experiments among various sparse datasets, where the time and space complexity are also analyzed.