ABSTRACT Context-aware collaborative filtering (CACF) is an effective approach for adapting recommendations under users’ specific contextual situations and aims to improve predictive accuracy for Context-aware recommender systems (CARSs). Incorporating context in recommender systems (RSs) considering the equal importance to all contextual dimensions is not appropriate for seeking an intelligent and useful recommendation. In this paper, we propose a Real-coded Genetic Algorithm (RCGA) based CARS framework that exploits contextual pre-filtering and contextual modeling paradigms into CACF with appropriate context feature weights for enhancing accuracy as well as the diversity of the recommendation list. Further to alleviate the data sparsity, an effective missing value prediction (EMVP) algorithm is applied into proposed framework. The accuracy based on RCGA is compared with other two schemes: Support Vector Machine (SVM) and Particle Swarm Optimization (PSO), and RCGA has shown better results. Experimental results based on real-world datasets have clearly established the effectiveness of our proposed CARS schemes.

中文翻译：

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基于使用遗传算法的上下文加权和缓解数据稀疏性的有效上下文感知建议

摘要上下文感知协同过滤（CACF）是一种在用户特定上下文情况下调整推荐的有效方法，旨在提高上下文感知推荐系统（CARS）的预测准确性。考虑到所有上下文维度的同等重要性，在推荐系统 (RS) 中加入上下文并不适合寻求智能且有用的推荐。在本文中，我们提出了一种基于实数编码遗传算法 (RCGA) 的 CARS 框架，该框架将上下文预过滤和上下文建模范式利用到具有适当上下文特征权重的 CACF 中，以提高准确性以及推荐列表的多样性。为了进一步缓解数据稀疏性，将有效的缺失值预测（EMVP）算法应用于所提出的框架。基于RCGA的精度与支持向量机（SVM）和粒子群优化（PSO）这两种方案进行比较，RCGA表现出更好的效果。基于真实世界数据集的实验结果清楚地证明了我们提出的 CARS 方案的有效性。