Spark平台下基于上下文信息的影片混合推荐

响应速度较慢和推荐内容与用户上下文信息匹配程度低是当前影片推荐系统迫切需要解决的问题。针对上述挑战，提出Spark平台下基于上下文信息的影片混合推荐方法。它利用分布式并行计算技术Spark进行加速，来提高系统对于海量数据的检索与计算速度，从而减少了系统响应时间。同时该方法将“上下文推荐”和“交替最小二乘的协同过滤（ALS）”融合成一种混合推荐方法，提高了系统的推荐精度。实验结果表明，所提出的混合推荐方法有不错的效果。

Context-aware movie hybrid recommendation on Spark platform

Slow response and recommended movies inconsistent with the users’ requests are key urgent problems in current movie recommendation system. To address this problem, a context-aware movie hybrid recommendation method on Spark platform is proposed. The method takes advantage of Spark, a distributed parallel computing technology, to improve retrieval and calculation speed for mass data, which reduces the response time of the recommendation system. At the same time, it fuses the user’s context information and ALS（Alternating Least Squares of collaborative filtering） to a hybrid recommendation method, which improves the recommendation accuracy of system. The results show that our method has a better performance than others.