Faculty of Computing & Informatics, Multimedia University, Cyberjaya, Selangor, 63100, Malaysia.
AirAsia Berhad, KLIA, Selangor, 64000, Malaysia.
F1000Res. 2021 Sep 17;10:937. doi: 10.12688/f1000research.73060.1. eCollection 2021.
A recommender system captures the user preferences and behaviour to provide a relevant recommendation to the user. In a hybrid model-based recommender system, it requires a pre-trained data model to generate recommendations for a user. Ontology helps to represent the semantic information and relationships to model the expressivity and linkage among the data. We enhanced the matrix factorization model accuracy by utilizing ontology to enrich the information of the user-item matrix by integrating the item-based and user-based collaborative filtering techniques. In particular, the combination of enriched data, which consists of semantic similarity together with rating pattern, will help to reduce the cold start problem in the model-based recommender system. When the new user or item first coming into the system, we have the user demographic or item profile that linked to our ontology. Thus, semantic similarity can be calculated during the item-based and user-based collaborating filtering process. The item-based and user-based filtering process are used to predict the unknown rating of the original matrix. Experimental evaluations have been carried out on the MovieLens 100k dataset to demonstrate the accuracy rate of our proposed approach as compared to the baseline method using (i) Singular Value Decomposition (SVD) and (ii) combination of item-based collaborative filtering technique with SVD. Experimental results demonstrated that our proposed method has reduced the data sparsity from 0.9542% to 0.8435%. In addition, it also indicated that our proposed method has achieved better accuracy with Root Mean Square Error (RMSE) of 0.9298, as compared to the baseline method (RMSE: 0.9642) and the existing method (RMSE: 0.9492). Our proposed method enhanced the dataset information by integrating user-based and item-based collaborative filtering techniques. The experiment results shows that our system has reduced the data sparsity and has better accuracy as compared to baseline method and existing method.
推荐系统通过捕捉用户偏好和行为，为用户提供相关推荐。在基于混合模型的推荐系统中，它需要一个预训练的数据模型来为用户生成推荐。本体论有助于表示语义信息和关系，以对数据之间的表达能力和链接进行建模。我们通过利用本体论来丰富用户-项目矩阵的信息，将基于项目和基于用户的协同过滤技术集成到矩阵分解模型中，从而提高了矩阵分解模型的准确性。特别是，包含语义相似度和评分模式的丰富数据的组合，将有助于减少基于模型的推荐系统中的冷启动问题。当新用户或新项目首次进入系统时，我们有用户人口统计数据或与我们的本体论相关的项目资料。因此，可以在基于项目和基于用户的协同过滤过程中计算语义相似度。基于项目和基于用户的过滤过程用于预测原始矩阵的未知评分。我们在 MovieLens 100k 数据集上进行了实验评估，以证明与基线方法（使用（i）奇异值分解（SVD）和（ii）基于项目的协同过滤技术与 SVD 的组合）相比，我们的方法的准确率。实验结果表明，我们的方法将数据稀疏度从 0.9542%降低到 0.8435%。此外，与基线方法（RMSE：0.9642）和现有方法（RMSE：0.9492）相比，我们的方法还实现了更好的准确性，其均方根误差（RMSE）为 0.9298。我们的方法通过集成基于用户和基于项目的协同过滤技术增强了数据集信息。实验结果表明，与基线方法和现有方法相比，我们的系统减少了数据稀疏性，并且具有更高的准确性。
