Semi-supervised collaborative filtering ensemble
Semi-supervised collaborative filtering ensemble
复制标题
DOI：
10.1007/s11280-021-00866-7
复制
发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Jun Wu;Xiankai Sang;Wei Cui
中科院分区：
文献类型：
--
作者：
Jun Wu;Xiankai Sang;Wei Cui
协同过滤（CF）在推荐系统中起着核心作用，但经常受到数据稀疏性问题的困扰，严重降低了推荐性能。在本文中，我们提出了一种半监督集成过滤（SSEF）方法，通过在一个共同训练框架中组装三种流行的CF技术来提高推荐性能。具体而言，SSEF首先通过三种不同的CF算法独立初始化带有标记样例的三个弱预测器。然后将邻域方法生成的两个预测器与潜在因素模型生成的剩余预测器合并，作为两个基本推荐器，在共同训练过程中，每个推荐器为另一个推荐器标记未标记的示例。为了安全开发未标记数据，通过验证伪标记样本对已标记样本的影响来估计标记置信度。最后的预测是通过混合三个增强了未标记数据的预测器的输出来完成的。在三个公共基准上进行的大量实验通过与许多最先进的CF技术（包括半监督、集成和基于侧信息的解决方案）进行比较，证明了所提出的SSEF的有效性。
Collaborative filtering (CF) plays a central role in recommender systems, but often suffers from the data sparsity issue that dramatically degrades the recommendation performance. In this paper, we propose a Semi-Supervised Ensemble Filtering (SSEF) method to improve the recommendation performance by assembling three popular CF techniques in a co-training framework. Concretely, SSEF first initializes three weak predictors with labeled examples by three different CF algorithms independently. Two predictors generated by neighborhood methods are then merged, along with the remaining one generated by latent factor model, serve as two base recommenders, each of which labels the unlabeled examples for the other recommender during the co-training process. To exploit unlabeled data safely, the labeling confidence is estimated by validating the influence of the pseudo-labeled examples on the labeled ones. The final prediction is made by blending the outputs from the three predictors enhanced with unlabeled data. Extensive experiments on three public benchmarks demonstrate the effectiveness of the proposed SSEF by comparing to a number of state-of-the-art CF techniques, including semi-supervised, ensemble, and side-information based solutions.