A belief propagation based recommender system for online services
A belief propagation based recommender system for online services
复制标题
基于信念传播的在线服务推荐系统
DOI：
10.1145/1864708.1864751
复制
发表时间：
2010
影响因子：
5.3
通讯作者：
F. Fekri
中科院分区：
文献类型：
--
作者：
Erman Ayday;F. Fekri
在本文中，我们报告了迭代概率算法在推荐系统的设计和评估中的首次应用的进展。所提出的迭代推荐系统（简称 BPRS）基于置信传播，这是一种针对 Turbo 码和低密度奇偶校验（LDPC）码的强大解码算法。置信传播算法依赖于为推荐系统适当选择的因子图的基于图的表示。推荐系统的因子图表示是一个二分图，其中用户和产品被排列为通过一些边连接的两组变量和因子节点。针对每个特定用户的推荐（预测评级）可以通过图中节点之间传递的概率消息来计算。我们使用 MovieLens 数据集通过计算机模拟对 BPRS 进行评估。我们观察到 BPRS 迭代地减少了用户预测评分的误差，直到收敛。此外，我们的初步结果表明平均误差 (MAE) 和均方根误差 (RMSE) 相对于项目平均有所改善。因此，我们相信信念传播是一种新的有前途的方法，它将为推荐系统提供鲁棒性和准确性。
In this paper we report our progress in the first application of iterative probabilistic algorithms in the design and evaluation of recommender systems. The proposed iterative recommender system (referred to as BPRS) is based on the belief propagation, a powerful decoding algorithm for turbo codes and Low-Density Parity-Check (LDPC) codes. The belief propagation algorithm relies on a graph-based representation of an appropriately chosen factor graph for the recommender systems. The factor graph representation of the recommender systems turned out to be a bipartite graph, where the users and products are arranged as two sets of variable and factor nodes that are connected via some edges. Recommendations (predicted ratings) for each particular user can be computed by probabilistic message passing between nodes in the graph. We provide an evaluation of BPRS via computer simulations using the MovieLens dataset. We observed that BPRS iteratively reduces the error in the predicted ratings of the users until it converges. Further, our initial results indicate an improvement in the Mean Average Error (MAE) and Root Mean Square Error (RMSE) over the Item Averaging. Therefore, we are confident that the belief propagation is a new promising approach which will offer robustness and accuracy for the recommender systems.