论文标题
基于文本的情感意识推荐
Text-based Emotion Aware Recommender
论文作者
论文摘要
我们将用户情感媒介（UVEC）和电影情感向量（MVEC）的概念应用于情感意识到的推荐系统的组成部分。我们建立了一个比较平台，该平台由五个推荐人组成，基于基于内容和协作过滤算法。我们采用了一条推文情感分类器，通过电影概述对电影的情感概况进行分类。我们从电影情感概况中构建MVEC。我们跟踪用户的电影观看历史记录，以通过用户观看的所有电影中的所有MVEC的平均值来制定UVEC。借助MVEC，我们构建了一种情感意识到的推荐人作为比较平台的算法之一。我们评估了这些推荐人产生的最重要的建议列表，并发现情感意识到的推荐人的顶级列表显示了偶然性建议。
We apply the concept of users' emotion vectors (UVECs) and movies' emotion vectors (MVECs) as building components of Emotion Aware Recommender System. We built a comparative platform that consists of five recommenders based on content-based and collaborative filtering algorithms. We employed a Tweets Affective Classifier to classify movies' emotion profiles through movie overviews. We construct MVECs from the movie emotion profiles. We track users' movie watching history to formulate UVECs by taking the average of all the MVECs from all the movies a user has watched. With the MVECs, we built an Emotion Aware Recommender as one of the comparative platforms' algorithms. We evaluated the top-N recommendation lists generated by these Recommenders and found the top-N list of Emotion Aware Recommender showed serendipity recommendations.
