论文标题
使用媒体内容元数据的情感功能来获得更好的电影建议
Using Affective Features from Media Content Metadata for Better Movie Recommendations
论文作者
论文摘要
本文通过用户的情感概况研究了电影建议决策的因果关系。我们主张一种通过自动检测电影概述中情感功能的自动检测将情感标签分配给电影的方法。我们应用了基于文本的情感检测和识别模型，该模型通过推文训练了简短的消息并传输了学习的模型，以检测电影概述的隐性情感特征。我们对情感电影标签进行了介绍，以代表电影的情绪嵌入。我们通过获取用户观看的所有电影情感向量的平均值来获得用户的情感功能。我们应用五距离指标来对用户的情绪概况的顶级电影建议进行排名。我们发现余弦相似性距离指标的性能要比其他距离指标衡量标准更好。我们得出的结论是，通过替换推荐人产生的顶级建议，用余弦相似性距离指标列出的重新推荐列表，用户将有效地获得情感意识到的顶级建议，同时使推荐人感觉像是一种情感意识到的建议。
This paper investigates the causality in the decision making of movie recommendations through the users' affective profiles. We advocate a method of assigning emotional tags to a movie by the auto-detection of the affective features in the movie's overview. We apply a text-based Emotion Detection and Recognition model, which trained by tweets short messages and transfers the learned model to detect movie overviews' implicit affective features. We vectorized the affective movie tags to represent the mood embeddings of the movie. We obtain the user's emotional features by taking the average of all the movies' affective vectors the user has watched. We apply five-distance metrics to rank the Top-N movie recommendations against the user's emotion profile. We found Cosine Similarity distance metrics performed better than other distance metrics measures. We conclude that by replacing the top-N recommendations generated by the Recommender with the reranked recommendations list made by the Cosine Similarity distance metrics, the user will effectively get affective aware top-N recommendations while making the Recommender feels like an Emotion Aware Recommender.
