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Aging Residual Factorization Machines: A Multi-Layer Residual Network Based on Aging Mechanisms

With the rapid development of recommendation systems, models and algorithms supporting the core of recommendation systems have emerged one after another, and researchers have attempted to optimize them. However, the structure of these models is complex. Popular deep neural networks often achieve the highest utilization of data by increasing the number of hidden layers, ignoring the problems of exploding and vanishing gradients and even the entire degradation of the networks. However, researchers pay too much attention to algorithms and models and do not consider the dataset itself. Methods for processing data and finding possible connections between the data and models have become new explorable points. Cold start is also a problem that researchers have been trying to solve and optimize since the birth of the recommendation system. Recent studies also provide good ideas for solving cold start, but the problem is that researchers still do not focus on datasets. In order to fill the gap in the exploration and research of datasets, this paper takes the long tail distribution and cold start problems that are common in recommendation systems such as the starting point, combines the residual network in computer vision with deep learning, and proposes the aging mechanism of datasets. In this paper, a multi-layer residual network based on aging mechanisms called Aging Residual Factorization Machines (ARFM) is proposed. Parallel experiments with other model algorithms are carried out on three datasets of different sizes and categories. Experimental results show that ARFM achieve performance advantages under the premise of different recommendation tasks.