基于Hadoop的IPTV隐式评分模型

根据网路协定电视（IPTV）用户收视行为数据中的隐式特性，提出一种新型的隐式评分模型。首先，介绍了IPTV用户收视行为数据的主要特点，提出一种新的用户收视比值、用户兴趣偏置因子以及视频类型影响因子相结合的多特征混合隐式评分模型；然后，提出基于收视时长和收视比值的收视行为筛选策略；最后，设计并实现了基于Hadoop的分布式模型架构。实验结果表明，所提模型有效提高了IPTV系统中推荐结果的质量，同时提升了时间效率，对于大规模数据有良好的可扩展性。

IPTV implicit scoring model based on Hadoop

According to the implicit characteristics of IPTV (Internet Protocol Television) user viewing behavior data, a novel implicit rating model was proposed. Firstly, the main features of IPTV user viewing behavior data were introduced, and a new mixed feature implicit scoring model was proposed, which combined with viewing ratio, user interest bias factor and video type influence factor. Secondly, the strategy of viewing behavior based on viewing time and viewing ratio was proposed. Finally, a distributed model architecture based on Hadoop was designed and implemented. The experimental results show that the proposed novel model effectively improves the quality of the recommended results in the IPTV system, improves the time efficiency, and has good scalability for large amounts of data.