Abstract:

Integrating with learning to rank methods, the authors propose a movie ranking prediction model by mining and analyzing the data from movie media websites, which includes extracting and expanding features related to ranking prediction as well as dividing and aligning ranking labels etc. Experiment results show that the proposed model effectively improves the performance of the movie ranking prediction task, which can benefit the cinemas to arrange the number of screenings properly. The model can also provide high quality recommendations to movies for the fans.

Key words: prediction of movie ranking, learning to rank, data mining, movie media website

摘要：

结合排序学习方法, 对电影排名预测任务进行研究。通过挖掘和分析电影媒体网站数据, 完成对排名预测相关特征的抽取与扩展及排名标注的对齐和划分等, 并提出面向电影媒体网站的排名预测模型。实验结果显示, 该模型能有效地提高电影排名预测任务的性能, 在为影视院线合理规划同期电影的上映时间及排片比例、为观影者提供优质热门的电影推荐等方面具有一定的应用价值。

关键词: 电影排名预测, 排序学习, 数据挖掘, 电影媒体网站