Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand.
Advanced Virtual and Intelligent Computing Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand.
PLoS One. 2022 May 5;17(5):e0266512. doi: 10.1371/journal.pone.0266512. eCollection 2022.
One of the most popular recommender system techniques is collaborative filtering (CF). Nowadays, many researchers apply a neural network with CF, but few focus on the neighbors' concept of CF. This work needs to consider two major issues: the similarity levels between the neighbors and the target user and the user's rating pattern conversion. Because different neighbors have a different influence on the target user and different users usually have a different rating pattern, the ratings directly utilized by the neighbor's preference pattern may be incorrect for the target user. Under two main issues, we try to accomplish three main ideas of CF's prediction: the similarity between users' levels, the neighbor's rating, and the rating conversion. Thus, we propose three main modules, the rating conversion module, the similarity module, and the prediction module, to solve the two issues mentioned above. The rating conversion module projects the neighbor's rating into the target user's aspect. The similarity module uses the users' attentions to compute similarity levels between users. Finally, these similarity levels and the converted ratings are integrated to perform the prediction. The proposed method is compared with the current CF with friends and latent factor model using two types of datasets: real-world and synthetic datasets. We evaluate N neighbors and all neighbors on real-world datasets to prove the number of neighbor is important. Moreover, the performance of the rating conversion module is also evaluated. The proposed method simulates the full rating datasets and the partial rating dataset to compare the effectiveness of using different types of distribution and dataset size. The experimental results demonstrate that the proposed method effectively outperformed the baselines using ranking evaluation and prediction accuracy on real-world and synthetic datasets. Besides, The effectiveness of using different the number of neighbors depends on the quality of neighbors.
最受欢迎的推荐系统技术之一是协同过滤（CF）。如今，许多研究人员将神经网络与 CF 结合使用，但很少关注 CF 的邻居概念。这项工作需要考虑两个主要问题：邻居与目标用户之间的相似性水平以及用户的评分模式转换。由于不同的邻居对目标用户的影响不同，不同的用户通常具有不同的评分模式，因此邻居的偏好模式直接利用的评分可能对目标用户不正确。在这两个主要问题下，我们尝试完成 CF 预测的三个主要思想：用户水平之间的相似性、邻居的评分和评分转换。因此，我们提出了三个主要模块，即评分转换模块、相似性模块和预测模块，以解决上述两个问题。评分转换模块将邻居的评分投射到目标用户的方面。相似性模块使用用户注意力计算用户之间的相似性水平。最后，将这些相似性水平和转换后的评分集成在一起进行预测。该方法与当前的 CF 与朋友和潜在因素模型使用两种类型的数据集进行比较：真实数据集和合成数据集。我们在真实数据集上评估 N 个邻居和所有邻居，以证明邻居数量的重要性。此外，还评估了评分转换模块的性能。该方法模拟了完整的评分数据集和部分评分数据集，以比较使用不同类型的分布和数据集大小的有效性。实验结果表明，该方法在真实数据集和合成数据集上使用排名评估和预测精度有效地优于基线。此外，使用不同数量邻居的有效性取决于邻居的质量。
