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Combining different metadata views for better recommendation accuracy

机译：结合不同的元数据视图以获得更好的推荐准确性

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摘要

Recommender systems emerged as means to help users deal with information overload by filtering content based on their preferences. Regardless of the recommendation method, there has been a recent interest in using user reviews as source of information, since they contain both detailed items' descriptions as well as users' opinions. Even though several works have been done in the subject, very few of them consider different views that the items may have towards their features, selecting only a method of weighting their features. In this work, we propose a system that combines two item representations that represent different views of the same feature set: one based on its statistics and the other based on its quality. Features are disambiguated concepts extracted from users' reviews. We propose several strategies divided into three categories: pre combination, neighborhood combination and post combination. We evaluate our strategies in two data sets, comparing them with each other and against the isolated item representations, as well as a representation baseline based on terms and sentiment analysis. Results are promising showing that some combinations are capable of producing better rankings than their isolated versions. (C) 2019 Elsevier Ltd. All rights reserved.
机译：推荐系统作为帮助用户根据其偏好过滤内容来处理信息过载的手段。无论推荐方法如何，最近有利于使用用户评论作为信息来源，因为它们包含详细的项目描述以及用户的意见。尽管在这个主题中已经完成了几种作品，但它们中的很少很少考虑该项目可能对其特征的不同视图，只选择加权其特征的方法。在这项工作中，我们提出了一个系统，该系统组合了两个项目表示表示相同功能的不同视图：一个基于其统计信息，基于其质量。功能是从用户评论中提取的歧义概念。我们提出了几项策略分为三类：预先组合，邻里组合和帖子组合。我们在两个数据集中评估我们的策略，将它们彼此相互比较并根据孤立的项目表示，以及基于术语和情绪分析的表示基线。结果表明，一些组合能够产生比其孤立版本更好的排名。 （c）2019 Elsevier Ltd.保留所有权利。

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