A recommender system is an information filtering technology that can be used to recommend items that may be of interest to users. Additionally, there are the context‐aware recommender systems that consider contextual information to generate the recommendations. Reviews can provide relevant information that can be used by recommender systems, including contextual and opinion information. In a previous work, we proposed a context‐aware recommendation method based on text mining (CARM‐TM). The method includes two techniques to extract context from reviews: CIET.5_embed, a technique based on word embeddings; and RulesContext, a technique based on association rules. In this work, we have extended our previous method by including CEOM, a new technique which extracts context by using aspect‐based opinions. We call our extension of CARM‐TOM (context‐aware recommendation method based on text and opinion mining). To generate recommendations, our method makes use of the CAMF algorithm, a context‐aware recommender based on matrix factorization. To evaluate CARM‐TOM, we ran an extensive set of experiments in a dataset about restaurants, comparing CARM‐TOM against the MF algorithm, an uncontextual recommender system based on matrix factorization; and against a context extraction method proposed in literature. The empirical results strongly indicate that our method is able to improve a context‐aware recommender system.

中文翻译：

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基于文本和观点挖掘的上下文感知推荐方法

推荐系统是一种信息过滤技术，可用于推荐用户可能感兴趣的项目。此外，还有一些上下文感知推荐器系统会考虑上下文信息以生成推荐。评论可以提供推荐系统可以使用的相关信息，包括上下文和意见信息。在先前的工作中，我们提出了一种基于文本挖掘的上下文感知推荐方法（CARMTM）。该方法包括两种从评论中提取上下文的技术：CIET.5 _embed，一种基于单词嵌入的技术；和RulesContext，这是一种基于关联规则的技术。在这项工作中，我们通过包括CEOM扩展了以前的方法，这是一种使用基于方面的观点来提取上下文的新技术。我们称其为CARM‐TOM（基于文本和观点挖掘的上下文感知推荐方法）的扩展。为了生成推荐，我们的方法利用了CAMF算法，它是基于矩阵分解的上下文感知推荐器。为了评估CARM‐TOM，我们在关于餐馆的数据集中进行了广泛的实验，将CARM‐TOM与基于矩阵分解的无上下文推荐系统MF算法进行了比较。反对文献中提出的上下文提取方法。实证结果强烈表明，我们的方法能够改善上下文感知推荐器系统。