Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India.
School of Information Technology and Engineering, VIT University, Vellore, Tamil Nadu, India.
PLoS One. 2023 Mar 15;18(3):e0282904. doi: 10.1371/journal.pone.0282904. eCollection 2023.
In today's society, time is considered more valuable than money, and researchers often have limited time to find relevant papers for their research. Identifying and accessing essential information can be a challenge in this situation. To address this, the personalized suggestion system has been developed, which uses a user's behavior data to suggest relevant items. The collaborative filtering strategy has been used to provide a user with the top research articles based on their queries and similarities with other users' questions, thus saving time by avoiding time-consuming searches. However, when rating data is abundant but sparse, the usual method of determining user similarity is relatively straightforward. Furthermore, it fails to account for changes in users' interests over time resulting in poor performance. This research proposes a new similarity measure approach that takes both user confidence and time context into account to increase user similarity computation. The experimental results show that the proposed technique works well with sparse data, and improves accuracy by 16.2% compared to existing models, especially during prediction. Furthermore, it enhances the quality of recommendations.
在当今社会，时间比金钱更有价值，研究人员通常只有有限的时间来为他们的研究找到相关的论文。在这种情况下，识别和访问必要的信息可能是一项挑战。为了解决这个问题，个性化建议系统已经被开发出来，它使用用户的行为数据来建议相关的项目。协同过滤策略被用来根据用户的查询和与其他用户问题的相似性，为用户提供顶级研究文章，从而通过避免耗时的搜索来节省时间。然而，当评分数据丰富但稀疏时，确定用户相似性的常用方法相对简单。此外，它没有考虑到用户兴趣随时间的变化，导致性能不佳。本研究提出了一种新的相似性度量方法，同时考虑用户的置信度和时间上下文，以增加用户相似性的计算。实验结果表明，该技术在稀疏数据上表现良好，与现有模型相比，准确性提高了 16.2%，特别是在预测时。此外，它还提高了推荐的质量。
