Spark平台下基于上下文信息的影片混合推荐
发布时间：2018-08-27 20:32
【摘要】：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。
[Abstract]:Slow response speed and low matching degree between recommendation content and user context information are the urgent problems that need to be solved in the current video recommendation system. In view of the above challenges, this paper proposes a mixed recommendation method based on context information in Spark platform. It uses distributed parallel computing technology (Spark) to speed up the retrieval and computation of massive data, thus reducing the response time of the system. At the same time, the "context recommendation" and "alternating least squares collaborative filtering (ALS)" are combined into a hybrid recommendation method, which improves the recommendation accuracy of the system. The experimental results show that the proposed hybrid recommendation method has a good effect.
【作者单位】： 武汉大学计算机学院软件工程国家重点实验室;湖北第二师范学院计算机学院;
【基金】：国家自然科学基金(No.61572374,No.U1135005)
【分类号】：TP391.3
，
本文编号：2208375
[Abstract]:Slow response speed and low matching degree between recommendation content and user context information are the urgent problems that need to be solved in the current video recommendation system. In view of the above challenges, this paper proposes a mixed recommendation method based on context information in Spark platform. It uses distributed parallel computing technology (Spark) to speed up the retrieval and computation of massive data, thus reducing the response time of the system. At the same time, the "context recommendation" and "alternating least squares collaborative filtering (ALS)" are combined into a hybrid recommendation method, which improves the recommendation accuracy of the system. The experimental results show that the proposed hybrid recommendation method has a good effect.
【作者单位】： 武汉大学计算机学院软件工程国家重点实验室;湖北第二师范学院计算机学院;
【基金】：国家自然科学基金(No.61572374,No.U1135005)
【分类号】：TP391.3
，
本文编号：2208375
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