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A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews

机译：基于从用户评论中提取的项目方面意见的推荐系统的比较分析

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摘要

In popular applications such as e-commerce sites and social media, users provide online reviews giving personal opinions about a wide array of items, such as products, services and people. These reviews are usually in the form of free text, and represent a rich source of information about the users' preferences. Among the information elements that can be extracted from reviews, opinions about particular item aspects (i.e., characteristics, attributes or components) have been shown to be effective for user modeling and personalized recommendation. In this paper, we investigate the aspect-based top-N recommendation problem by separately addressing three tasks, namely identifying references to item aspects in user reviews, classifying the sentiment orientation of the opinions about such aspects in the reviews, and exploiting the extracted aspect opinion information to provide enhanced recommendations. Differently to previous work, we integrate and empirically evaluate several state-of-the-art and novel methods for each of the above tasks. We conduct extensive experiments on standard datasets and several domains, analyzing distinct recommendation quality metrics and characteristics of the datasets, domains and extracted aspects. As a result of our investigation, we not only derive conclusions about which combination of methods is most appropriate according to the above issues, but also provide a number of valuable resources for opinion mining and recommendation purposes, such as domain aspect vocabularies and domain-dependent, aspect-level lexicons.
机译：在诸如电子商务网站和社交媒体之类的流行应用程序中，用户提供在线评论，这些评论给出了对各种商品（例如产品，服务和人员）的个人意见。这些评论通常采用自由文本的形式，代表了有关用户偏好的丰富信息来源。在可以从评论中提取的信息元素中，关于特定项目方面的意见（即特征，属性或组件）已被证明对用户建模和个性化推荐有效。在本文中，我们通过分别解决以下三个任务来研究基于方面的前N个推荐问题：识别用户评论中对项目方面的引用，对评论中有关这些方面的观点的情感取向进行分类以及利用提取的方面意见信息以提供增强的建议。与以前的工作不同，我们针对上述每个任务整合并凭经验评估了几种最新的和新颖的方法。我们对标准数据集和多个领域进行了广泛的实验，分析了不同的推荐质量指标以及数据集，领域和提取方面的特征。作为我们调查的结果，我们不仅得出关于根据上述问题哪种方法最合适的结论，而且还提供了大量有价值的资源用于意见挖掘和推荐目的，例如领域方面的词汇表和领域相关的词汇，方面级别的词典。

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