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:最近,深神经网络(DNNS)被广泛引入合作过滤系统(CF),以便产生更准确的建议结果。 但是,基于DNNS的模式通常具有很高的计算复杂性,即耗用非常长的培训时间并储存大量可培训参数。为了解决这些问题,我们提议了一个新的广泛的建议系统,称为“宽合作过滤系统(BroadCF)”,这是一种高效的非线性合作过滤方法。使用DNS,而使用宽学习系统(BLS)作为一种绘图功能,以了解用户和项目之间复杂的非线性非线性关系,这可以避免上述问题,同时实现非常令人满意的建议性能。然而,将原评级数据直接输入BLS不可行。为此,我们提议采用用户项目评级协作矢量预处理程序,以生成低维用户项目输入数据,这能够利用最相似用户/项目的质量判断。在七个基准数据集上进行的广泛实验,证实了拟议的广域CFLS算算法的有效性。