使用BP神经网络缓解协同过滤推荐算法的稀疏性问题

Employing BP Neural Networks to Alleviate the Sparsity Issue in Collaborative Filtering Recommendation Algorithms

推荐质量低是协同过滤推荐技术面临的主要难题之一.数据集的极端稀疏是造成推荐质量低的主要原因之一.常见的降维法和智能Agent法虽然某种程度上能缓解这个问题,但会导致信息损失和适应性等问题.设计了一个新的协同过滤算法,根据用户评分向量交集大小选择候选最近邻居集,采用BP神经网络预测用户对项的评分,减小候选最近邻数据集的稀疏性.该算法避免了降维法和智能Agent法的缺点,而且实验结果表明,该方法能提高预测值的准确度,从而提高协同过滤推荐系统的推荐质量.

Poor recommendation quality is one major challenge in collaborative filtering recommender systems. Sparsity of source data sets is one major reason causing the poor quality. The popular singular value decomposition techniques and the agent-based methods to a certain extent are able to alleviate this issue. But at the same time they also introduce new problems. To reduce sparsity, a novel collaborative filtering algorithm is designed, which firstly selects users whose non-null ratings intersect the most as candidates of nearest neighbors, and then builds up backpropagation neural networks to predict values of the null ratings in the candidates. Experiments are conducted based on standard dataset. The results show that this methodology is able to increase the accuracy of the predicted values, resulting in improving recommendation quality of the collaborative filtering recommendation algorithm.