Aspect-Based Summarization: An Approach With Different Levels of Details to Explain Recommendations

Luan Soares de Souza, M. Manzato
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引用次数: 2

Abstract

Recommender systems have become crucial since they appeared, helping users make decisions. Commonly, recommendation algorithms use the historical interaction data between users and items to predict the users’ tastes and suggest new items. However, offering recommendations sometimes is insufficient for the user to make a decision. In this way, the recommendations’ explanation to support the decision-making process has been considered an essential property. The explanations of recommendations can be generated from different resources, such as unstructured data (e.g., users’ reviews), and presented in many ways, such as summarization. However, offering static explanations may not be helpful in several situations. For example, some users familiar with the content may be willing to receive explanations with fewer details than others who are not acquainted with the domain. In this context, we an approach propose to generate summaries with different levels of detail as post-hoc explanations. We used an aspect-based extractive summarization approach with hierarchical clustering of aspects to select sentences from users’ reviews. Then, this hierarchical structure is used to create explanations of recommended items with different lengths, depending on the user’s preferences. Our dynamic explanation system was evaluated against two state-of-art baselines, and the results are promising.
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基于方面的总结:用不同层次的细节来解释建议的方法
推荐系统自出现以来就变得至关重要,它可以帮助用户做出决定。通常,推荐算法使用用户和商品之间的历史交互数据来预测用户的口味并推荐新商品。然而,提供推荐有时不足以让用户做出决定。这样,支持决策过程的建议解释被认为是一项基本属性。推荐的解释可以从不同的资源生成,例如非结构化数据(例如,用户的评论),并以多种方式呈现,例如摘要。然而,在某些情况下,提供静态的解释可能没有帮助。例如,一些熟悉内容的用户可能比其他不熟悉该领域的用户更愿意接受包含较少细节的解释。在这种情况下,我们提出了一种方法,即生成具有不同细节级别的摘要,作为事后解释。我们使用了一种基于方面的提取摘要方法,通过方面的分层聚类从用户评论中选择句子。然后,根据用户的偏好,使用这个层次结构来创建不同长度的推荐项目的解释。我们的动态解释系统在两个最先进的基线上进行了评估,结果是有希望的。
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