Recommender systems help users to deal with the information overload problem by producing personalized content according to their interests. Beyond the traditional recommender strategies, there is a growing effort to incorporate users’ reviews into the recommendation process, since they provide a rich set of information regarding both items’ features and users’ preferences. This article proposes a recommender system that uses users’ reviews to produce items’ representations that are based on the overall sentiment toward the items’ features. We focus on exploiting the impact that different feature extraction techniques, allied with sentiment analysis, cause in an item attribute-aware neighborhood-based recommender algorithm. We compare four techniques of different granularities (terms and aspects) in two recommendation scenarios (rating prediction and item recommendation) and elect the most promising technique. We also compare our techniques with traditional structured metadata constructions, which are used as the baseline in our experimental evaluation. The results show that the techniques based on terms provide better results, since they produce a larger set of features, hence detailing better the items.

中文翻译：

---

基于用户评论的特征提取技术进行电影推荐

推荐系统通过根据用户的兴趣产生个性化的内容来帮助用户处理信息过载问题。除了传统的推荐策略之外，越来越多的努力将用户的评论纳入推荐过程，因为它们提供了关于项目特征和用户偏好的丰富信息集。本文提出了一个推荐系统，该系统使用用户的评论来生成基于对项目特征的整体情绪的项目表示。我们专注于利用不同的特征提取技术与情感分析相结合，在基于项目属性的基于邻域的推荐算法中产生的影响。我们在两个推荐场景（评分预测和项目推荐）中比较了四种不同粒度（术语和方面）的技术，并选择了最有前途的技术。我们还将我们的技术与传统的结构化元数据结构进行比较，后者在我们的实验评估中用作基线。结果表明，基于术语的技术提供了更好的结果，因为它们产生了更大的特征集，因此可以更好地详细说明项目。