Tokala Srilatha, Enduri Murali Krishna, Lakshmi T Jaya, Sharma Hemlata
Algorithms and Complexity Theory Lab, Department of Computer Science and Engineering, SRM University-AP, Amaravati 522502, India.
Department of Computing, Sheffield Hallam University, Howard Street, Sheffield S1 1WB, UK.
Entropy (Basel). 2023 Sep 20;25(9):1360. doi: 10.3390/e25091360.
Matrix factorization is a long-established method employed for analyzing and extracting valuable insight recommendations from complex networks containing user ratings. The execution time and computational resources demanded by these algorithms pose limitations when confronted with large datasets. Community detection algorithms play a crucial role in identifying groups and communities within intricate networks. To overcome the challenge of extensive computing resources with matrix factorization techniques, we present a novel framework that utilizes the inherent community information of the rating network. Our proposed approach, named Community-Based Matrix Factorization (CBMF), has the following steps: (1) Model the rating network as a complex bipartite network. (2) Divide the network into communities. (3) Extract the rating matrices pertaining only to those communities and apply MF on these matrices in parallel. (4) Merge the predicted rating matrices belonging to communities and evaluate the root mean square error (RMSE). In our experimentation, we use basic MF, SVD++, and FANMF for matrix factorization, and the Louvain algorithm is used for community division. The experimental evaluation on six datasets shows that the proposed CBMF enhances the quality of recommendations in each case. In the MovieLens 100K dataset, RMSE has been reduced to 0.21 from 1.26 using SVD++ by dividing the network into 25 communities. A similar reduction in RMSE is observed for the datasets of FilmTrust, Jester, Wikilens, Good Books, and Cell Phone.
矩阵分解是一种长期使用的方法，用于分析和从包含用户评分的复杂网络中提取有价值的见解推荐。当面对大型数据集时，这些算法所需的执行时间和计算资源会带来限制。社区检测算法在识别复杂网络中的群组和社区方面起着关键作用。为了用矩阵分解技术克服大量计算资源的挑战，我们提出了一个利用评分网络固有社区信息的新颖框架。我们提出的方法名为基于社区的矩阵分解（CBMF），有以下步骤：（1）将评分网络建模为复杂的二分网络。（2）将网络划分为社区。（3）提取仅与那些社区相关的评分矩阵，并并行地对这些矩阵应用矩阵分解。（4）合并属于社区的预测评分矩阵并评估均方根误差（RMSE）。在我们的实验中，我们使用基本矩阵分解、SVD++和FANMF进行矩阵分解，并使用Louvain算法进行社区划分。对六个数据集的实验评估表明，所提出的CBMF在每种情况下都提高了推荐质量。在MovieLens 100K数据集中，通过将网络划分为25个社区，使用SVD++时RMSE已从1.26降至0.21。对于FilmTrust、Jester、Wikilens、Good Books和手机数据集，也观察到了类似的RMSE降低。
