PNP:电影设计的快速路径集成方法PNP: Fast Path Ensemble Method for Movie Design |
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课程网址: | http://videolectures.net/kdd2017_koutra_movie_design/ |
主讲教师: | Danai Koutra |
开课单位: | 密歇根大学 |
开课时间: | 2017-10-09 |
课程语种: | 英语 |
中文简介: | 我们如何设计一款产品或电影来吸引宾夕法尼亚州青少年或自由派报纸评论家的兴趣?那部电影的类型应该是什么以及演员阵容应该是什么?在这项工作中,我们试图确定如何设计具有针对特定用户群体的功能的新电影。我们将电影设计表述为一个优化问题,涉及用户特征得分的推断以及最大化吸引用户数量的特征的选择。我们的方法 PNP 基于用户、电影和特征(例如演员、导演、流派)的异构三方图,其中用户对电影进行评分以及对电影做出贡献的特征。我们通过利用通过不同类型的关系定义的用户相似性来学习偏好,并表明我们的方法优于最先进的方法,包括矩阵分解和其他基于异构图的分析。我们根据公开的真实世界数据对 PNP 进行了评估,结果表明它具有高度可扩展性,并且可以有效地提供面向不同用户群体(包括男性、女性和青少年)的电影设计。 |
课程简介: | How can we design a product or movie that will attract, for example, the interest of Pennsylvania adolescents or liberal newspaper critics? What should be the genre of that movie and who should be in the cast? In this work, we seek to identify how we can design new movies with features tailored to a specific user population. We formulate the movie design as an optimization problem over the inference of user-feature scores and selection of the features that maximize the number of attracted users. Our approach, PNP, is based on a heterogeneous, tripartite graph of users, movies and features (e.g., actors, directors, genres), where users rate movies and features contribute to movies. We learn the preferences by leveraging user similarities defined through different types of relations, and show that our method outperforms state-of-the-art approaches, including matrix factorization and other heterogeneous graph-based analysis. We evaluate PNP on publicly available real-world data and show that it is highly scalable and effectively provides movie designs oriented towards different groups of users, including men, women, and adolescents. |
关 键 词: | 电影设计; 矩阵分解; 数据科学 |
课程来源: | 视频讲座网 |
数据采集: | 2023-12-25:wujk |
最后编审: | 2023-12-25:wujk |
阅读次数: | 15 |