Explicable recommendation model based on a time-assisted knowledge graph and many-objective optimization algorithm

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Concurrency and Computation-Practice & Experience Pub Date : 2024-06-25 DOI:10.1002/cpe.8210
Rui Zheng, Linjie Wu, Xingjuan Cai, Yubin Xu
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Abstract

Existing research on recommender systems primarily focuses on improving a single objective, such as prediction accuracy, often ignoring other crucial aspects of recommendation performance such as temporal factor, user satisfaction, and acceptance. To solve this problem, we proposed an explicable recommendation model using many-objective optimization and a time-assisted knowledge graph, which utilizes user interaction times within the graph to prioritize recommending recently frequently visited items and is further optimized using a many-objective optimization algorithm. In this model, the temporal weight of user actions at different times is first determined through a time decay function. Additionally, if a user clicks on the same item again, the current action's temporal weight is set to one. This strategy prioritizes recent user actions and frequently visited items, reflecting current interests and preferences better. Next, the created knowledge graph is used to create a list of potential recommendations. Embedding methods obtain the vectors for entities and relations in the path. These vectors, combined with the temporal weight of actions, quantify the explainability of user recommendations. Optimizing the rest of the recommendation performance with many objective algorithms while focusing on the user's recent frequent visits to the item. Finally, the outcomes of the research study indicate that, compared to other explicable recommended methods, our model, considering temporal factor, improved average accuracy by 11%, diversity by 1%, and explainability by 21% in the Useraction1 data set. Results in other data sets also indicate that the proposed model maintains accuracy, diversity, and novelty while enhancing explainability.

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基于时间辅助知识图谱和多目标优化算法的可解释推荐模型
摘要现有的推荐系统研究主要集中在提高预测准确率等单一目标上,往往忽略了推荐性能的其他重要方面,如时间因素、用户满意度和接受度等。为了解决这个问题,我们提出了一种使用多目标优化和时间辅助知识图谱的可解释推荐模型,该模型利用图谱中的用户交互时间优先推荐最近经常访问的项目,并使用多目标优化算法进一步优化。在该模型中,首先通过时间衰减函数确定用户在不同时间的操作的时间权重。此外,如果用户再次点击同一项目,当前操作的时间权重将设为 1。这种策略会优先考虑用户最近的操作和经常访问的项目,从而更好地反映用户当前的兴趣和偏好。接下来,创建的知识图谱将用于创建潜在推荐列表。嵌入方法可获得路径中实体和关系的向量。这些向量与行为的时间权重相结合,量化了用户推荐的可解释性。利用多种客观算法优化其余推荐性能,同时关注用户最近频繁访问的项目。最后,研究结果表明,与其他可解释性推荐方法相比,我们的模型考虑了时间因素,在 Useraction1 数据集中,平均准确率提高了 11%,多样性提高了 1%,可解释性提高了 21%。其他数据集的结果也表明,建议的模型在提高可解释性的同时,还保持了准确性、多样性和新颖性。
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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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Issue Information Improving QoS in cloud resources scheduling using dynamic clustering algorithm and SM-CDC scheduling model Issue Information Issue Information Camellia oleifera trunks detection and identification based on improved YOLOv7
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