推荐系统中稀疏情景预测的特征-类别交互因子分解机

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

随着Web信息的不断增长与发展,对用户稀疏行为的预测已成为目前推荐系统的研究热点.近年来,因子分解机(factorization machine, FM)的提出在一定程度上缓解了稀疏场景下预测精度不准确的问题.它的主要思想是通过2阶特征交互来获取特征间丰富的语义关系.随后,感知交互因子分解机(interaction-aware factorization machines, IFM)在FM的特征交互基础上引入类别交互的概念来扩展潜在的交互特性,通过把特征和类别分别进行交互后再融合来得到更准确的预测结果.在IFM的基础上,提出了一种特征-类别交互因子分解机(FIFM)模型.FIFM不仅保留了特征交互和类别交互机制,还设计了一种新的特征-类别交互机制(FIM)来进一步挖掘交互信息中的有效信息,并利用融合交互感知来预测不同稀疏场景下的用户行为模式.此外,还基于深度学习提出了一种实现FIFM的神经网络模型GFIM.相比于FIFM,GFIM的参数量和时间复杂度更高,但同时也能捕获更多高阶的非线性特征交互信息,能适合算力较高的应用场景.在4个真实数据集上的实验结果表明,FIFM和GFIM在RMSE指标上超越了当前最好的方法IFM.实验工作探究了多类稀疏场景下的预测结果,记录了时间和空间复杂度的消耗情况,并进行了分析讨论.

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.