ABSTRACT Recommender systems have become indispensable for services in the era of big data. To improve accuracy and satisfaction, context-aware recommender systems (CARSs) attempt to incorporate contextual information into recommendations. Typically, valid and influential contexts are determined in advance by domain experts or feature selection approaches. Most studies have focused on utilizing the unitary context due to the differences between various contexts. Meanwhile, multi-dimensional contexts will aggravate the sparsity problem, which means that the user preference matrix would become extremely sparse. Consequently, there are not enough or even no preferences in most multi-dimensional conditions. In this paper, we propose a novel framework to alleviate the sparsity issue for CARSs, especially when multi-dimensional contextual variables are adopted. Motivated by the intuition that the overall preferences tend to show similarities among specific groups of users and conditions, we first explore to construct one contextual profile for each contextual condition. In order to further identify those user and context subgroups automatically and simultaneously, we apply a co-clustering algorithm. Furthermore, we expand user preferences in a given contextual condition with the identified user and context clusters. Finally, we perform recommendations based on expanded preferences. Extensive experiments demonstrate the effectiveness of the proposed framework.

中文翻译：

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一种缓解上下文感知推荐系统中稀疏问题的新框架

摘要 在大数据时代，推荐系统已成为服务不可或缺的一部分。为了提高准确性和满意度，上下文感知推荐系统 (CARS) 尝试将上下文信息合并到推荐中。通常，有效且有影响力的上下文由领域专家或特征选择方法预先确定。由于各种语境之间的差异，大多数研究都集中在利用单一语境上。同时，多维上下文会加剧稀疏问题，这意味着用户偏好矩阵会变得非常稀疏。因此，在大多数多维条件下没有足够甚至没有偏好。在本文中，我们提出了一个新的框架来缓解 CARS 的稀疏性问题，特别是当采用多维上下文变量时。出于整体偏好倾向于在特定用户和条件组之间显示相似性的直觉，我们首先探索为每个上下文条件构建一个上下文配置文件。为了进一步自动和同时识别这些用户和上下文子组，我们应用了一种协同聚类算法。此外，我们使用已识别的用户和上下文集群在给定的上下文条件下扩展用户偏好。最后，我们根据扩展的偏好执行推荐。大量实验证明了所提出框架的有效性。我们首先探索为每个上下文条件构建一个上下文配置文件。为了进一步自动和同时识别这些用户和上下文子组，我们应用了一种协同聚类算法。此外，我们使用已识别的用户和上下文集群在给定的上下文条件下扩展用户偏好。最后，我们根据扩展的偏好执行推荐。大量实验证明了所提出框架的有效性。我们首先探索为每个上下文条件构建一个上下文配置文件。为了进一步自动和同时识别这些用户和上下文子组，我们应用了一种协同聚类算法。此外，我们使用已识别的用户和上下文集群在给定的上下文条件下扩展用户偏好。最后，我们根据扩展的偏好执行推荐。大量实验证明了所提出框架的有效性。