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首页> 外文期刊>Journal of Intelligent Information Systems >Combining similarity and sentiment in opinion mining for product recommendation
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Combining similarity and sentiment in opinion mining for product recommendation

机译：结合意见挖掘中的相似度和情感来推荐产品

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摘要

In the world of recommender systems, so-called content-based methods are an important approach that rely on the availability of detailed product or item descriptions to drive the recommendation process. For example, recommendations can be generated for a target user by selecting unseen products that are similar to the products that the target user has liked or purchased in the past. To do this, content-based methods must be able to compute the similarity between pairs of products (unseen products and liked products, for example) and typically this is achieved by comparing product features or other descriptive elements. The approach works well when product descriptions are readily available and when they are detailed enough to afford an effective similarity comparison. But this is not always the case. Detailed product descriptions may not be available since they can be expensive to create and maintain. In this article we consider another source of product descriptions in the form of the user-generated reviews that frequently accompany products on the web. We ask whether it is possible to mine these reviews, unstructured and noisy as they are, to produce useful product descriptions that can be used in a recommendation system. In particular we describe a novel approach to product recommendation that harnesses not only the features that can be mined from user-generated reviews but also the expressions of sentiment that are associated with these features. We present a recommendation ranking strategy that combines similarity and sentiment to suggest products that are similar but superior to a query product according to the opinion of reviewers, and we demonstrate the practical benefits of this approach across a variety of Amazon product domains.
机译：在推荐系统的世界中，所谓的基于内容的方法是一种重要的方法，它依赖于详细产品或项目描述的可用性来驱动推荐过程。例如，可以通过选择与目标用户过去喜欢或购买的产品相似的看不见的产品来为目标用户生成推荐。为此，基于内容的方法必须能够计算成对产品（例如，看不见的产品和喜欢的产品）之间的相似度，通常，这是通过比较产品功能或其他描述性元素来实现的。当容易获得产品描述并且其详细程度足以提供有效的相似性比较时，该方法会很好地工作。但这并非总是如此。详细的产品说明可能不可用，因为它们的创建和维护成本可能很高。在本文中，我们以用户生成的评论的形式考虑产品描述的另一个来源，这些评论经常伴随网络上的产品。我们询问是否有可能挖掘这些评论，这些评论既非结构化也很嘈杂，以产生可以在推荐系统中使用的有用的产品描述。特别是，我们描述了一种新颖的产品推荐方法，该方法不仅利用了可以从用户生成的评论中挖掘的功能，而且还利用了与这些功能相关的情感表达。根据审阅者的意见，我们提出了一种推荐评级策略，该策略结合了相似性和情感来建议相似但优于查询产品的产品，并且展示了该方法在各种亚马逊产品领域中的实际好处。

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