摘要
针对推荐算法未考虑大数据量计算导致系统性能差及基于历史评分的相似性不能反映用户兴趣动态变化的问题,提出了满足用户兴趣漂移的计算自适应快速推荐算法。该算法依据CPU等计算资源使用率动态调整待推荐用户窗口,并按项目类别及其访问热度动态分配计算时间,计算自适应项目与目标用户的优先级和相似性计算难易度,提高计算效率与响应速度;建立访问次数随时间变化的兴趣度量函数自适应用户兴趣漂移,提高推荐质量。实验结果表明,算法速度更快、推荐更准确、用户体验更好。
Aiming at recommender system＇ s problems that poor performance was caused by compute without considering the mass data and similarities based on history information couldn＇ t reflect the change of user interests, this paper proposed a com- pute adaptive fast recommendation algorithm meet user interests drift. The algorithm dynamically adjusted window＇ s length of the target users according to the computing resources usage rate such as CPU and allocates computation time in accordance with categories and access popularity of items, it achieved that computing automatically adapts the priority, similarity computation difficulty of items and target users,improved the computation efficiency and response speed. Moreover,the thesis created an in- terest measure function by access number changing over time to adapt interest drift, which improved the quality of the recom- mended. The experimental results show that the algorithm has faster speed, more accurate recommender precision and a better user experience.
出处
《计算机应用研究》
CSCD
北大核心
2015年第9期2669-2673,共5页
Application Research of Computers
基金
河北省高等学校科学技术研究重点项目(ZD2014061)
关键词
用户兴趣漂移
计算自适应
推荐算法
user interest drift
compute adaptive
recommendation algorithm
