Detecting beneficial feature interactions is essential in recommender systems, and existing approaches achieve this by examining all the possible feature interactions. However, the cost of examining all the possible higher-order feature interactions is prohibitive (exponentially growing with the order increasing). Hence existing approaches only detect limited order (e.g., combinations of up to four features) beneficial feature interactions, which may miss beneficial feature interactions with orders higher than the limitation. In this paper, we propose a hypergraph neural network based model named HIRS. HIRS is the first work that directly generates beneficial feature interactions of arbitrary orders and makes recommendation predictions accordingly. The number of generated feature interactions can be specified to be much smaller than the number of all the possible interactions and hence, our model admits a much lower running time. To achieve an effective algorithm, we exploit three properties of beneficial feature interactions, and propose deep-infomax-based methods to guide the interaction generation. Our experimental results show that HIRS outperforms state-of-the-art algorithms by up to 5% in terms of recommendation accuracy.
翻译:检测有益的特征互动在推荐者系统中至关重要,而现有方法通过审查所有可能的特征互动实现这一点。然而,审查所有可能的更高层次特征互动的成本是令人望而生畏的(随着订单的增加而迅速增加 ) 。因此,现有方法只能检测有限的顺序(例如,最多四个特征的组合) 有益特征互动,这可能错过与高于限制的订单的有益特征互动。在本文中,我们提议了一个以HIRS为主的超光谱神经网络模型。 HIRS是直接产生任意命令的有益特征互动并据此作出建议预测的首项工作。生成的特征互动数量可以比所有可能的互动数量少得多,因此,我们的模型承认运行时间要少得多。为了实现有效的算法,我们利用了有益的特征互动的三个属性,并提出以深信息轴为基础的方法来指导互动生成。我们的实验结果表明,HIRS在建议准确性方面比最新水平的算法高出5%。