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Combining different metadata views for better recommendation accuracy

机译：组合不同的元数据视图以提高推荐准确性

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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：推荐系统应运而生，它可以帮助用户根据自己的喜好过滤内容，从而解决信息过载的问题。不管推荐方法如何，近来人们都对使用用户评论作为信息源感兴趣，因为它们既包含详细的项目描述，也包含用户的意见。即使已经完成了该主题的几项工作，但很少有人考虑这些项目可能对它们的特征有不同的看法，仅选择一种加权其特征的方法。在这项工作中，我们提出了一个系统，该系统结合了两个表示相同功能集的不同视图的项表示：一个基于其统计信息，另一个基于其质量。功能是从用户评论中提取的概念。我们提出了几种策略，分为三类：预组合，邻域组合和后组合。我们在两个数据集中评估我们的策略，将它们彼此进行比较，并与孤立的项目表示形式进行比较，并根据条款和情感分析对表示形式基线进行比较。结果令人鼓舞，表明某些组合比单独的组合能够产生更好的排名。 （C）2019 Elsevier Ltd.保留所有权利。

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