Entity Ranking by Learning and Inferring Pairwise Preferences from User Reviews
Entity Ranking by Learning and Inferring Pairwise Preferences from User Reviews
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DOI：
10.1007/978-3-319-70145-5_11
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发表时间：
2017-11
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通讯作者：
Shinryo Uchida;Takehiro Yamamoto;Makoto P. Kato;H. Ohshima;Katsumi Tanaka
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文献类型：
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作者：
Shinryo Uchida;Takehiro Yamamoto;Makoto P. Kato;H. Ohshima;Katsumi Tanaka
在本文中，我们提出了一种基于从用户评论中学习和推断的成对偏好对实体（例如产品）进行排名的方法。我们提出的方法从用户评论中找到表示实体在某个属性方面的成对偏好的表达式，并学习一个确定属性相对程度的函数来对实体进行排名。由于评论中这样的表达式数量有限，我们进一步提出了一种基于从评论中获得的属性依赖关系推断成对偏好的方法。由于一些成对偏好的可信度较低，我们还提出了一种改进版的学习排序方法，模糊排序支持向量机，它可以考虑到成对偏好的不确定性。实验采用三种产品类别和每种类别的特定属性进行。实验结果表明，该方法可以比基线方法更准确地学习成对偏好，并且基于属性依赖的推理可以提高性能。
In this paper, we propose a method of ranking entities (e.g. products) based on pairwise preferences learned and inferred from user reviews. Our proposed method finds expressions from user reviews that indicate pairwise preferences of entities in terms of a certain attribute, and learns a function that determines the relative degree of the attribute to rank entities. Since there are a limited number of such expressions in reviews, we further propose a method of inferring pairwise preferences based on attribute dependencies obtained from reviews. As some pairwise preferences are less confident, we also propose a modified version of a learning to rank method, Fuzzy Ranking SVM, which can take into account the uncertainty of pairwise preferences. The experiment was carried out with three categories of products and several attributes specific to each category. The experimental results showed that our approach could learn more accurate pairwise preferences than baseline methods, and inference based on the attribute dependency could improve the performances.