Recently, Deep Neural Networks (DNNs) have been widely introduced into Collaborative Filtering (CF) to produce more accurate recommendation results due to their capability of capturing the complex nonlinear relationships between items and users.However, the DNNs-based models usually suffer from high computational complexity, i.e., consuming very long training time and storing huge amount of trainable parameters. To address these problems, we propose a new broad recommender system called Broad Collaborative Filtering (BroadCF), which is an efficient nonlinear collaborative filtering approach. Instead of DNNs, Broad Learning System (BLS) is used as a mapping function to learn the complex nonlinear relationships between users and items, which can avoid the above issues while achieving very satisfactory recommendation performance. However, it is not feasible to directly feed the original rating data into BLS. To this end, we propose a user-item rating collaborative vector preprocessing procedure to generate low-dimensional user-item input data, which is able to harness quality judgments of the most similar users/items. Extensive experiments conducted on seven benchmark datasets have confirmed the effectiveness of the proposed BroadCF algorithm

翻译：近年来，深度神经网络（DNNs）因其能够捕获用户与项目之间的复杂非线性关系，被广泛引入协同过滤（CF）中以生成更精确的推荐结果。然而，基于DNNs的模型通常面临高计算复杂性的问题，即训练时间极长且需存储大量可训练参数。为解决这些问题，我们提出了一种新型宽推荐系统——宽协同过滤（BroadCF），这是一种高效的非线性协同过滤方法。该方法利用宽度学习系统（BLS）替代DNNs作为映射函数，学习用户与项目之间的复杂非线性关系，从而在避免上述问题的同时获得令人满意的推荐性能。然而，直接将原始评分数据输入BLS并不可行。为此，我们提出了一种用户-项目评分协同向量预处理流程，用于生成低维的用户-项目输入数据，该流程能够有效利用最相似用户/项目的质量判断。在七个基准数据集上进行的广泛实验证实了所提出的BroadCF算法的有效性。