摘要
针对经典的热传导推荐算法准确度低以及物质扩散推荐算法多样性低的问题,提出一种基于热传导和物质扩散的混合推荐算法。考虑用户活跃度对推荐算法的影响,通过引入可调参数θ调节用户活跃度对推荐效果的影响,实现资源的重新分配,进而获得更好的推荐结果。实验结果表明,当θ取得最优值时,相比改进前的热传导和物质扩散混合算法,该算法在Netflix数据集上,精确率和多样性分别提高了约5.81%和4.15%。在MovieLens数据集上,精确率和多样性分别提高了约5.08%和3.60%。
For the problem of the classical Heat Conduction （HC） recommendation algorithm with low accuracy and the Mass Diffusion （MD） recommendation algorithm with low diversity, an improved hybrid recommendation algorithm based on HC and MD is proposed. Considering the influence of user activity on the recommendation algorithm, it introduces a tunable parameter θ to adjust the influence of user activity and reallocate resources so as to obtain better recommendation results. Experimental results show that the precision and diversity of the algorithm are increased by about 5.81% and 4.15% on the Netflix dataset,5.08% and 3.60% on the MovieLens dataset respectively,compared with the previous HC and MD hybrid algorithm, when parameter 0 obtains optimal value.
出处
《计算机工程》
CAS
CSCD
北大核心
2017年第3期247-252,共6页
Computer Engineering
基金
国家自然科学基金(60873200
90818028)
关键词
协同过滤
物质扩散
用户活跃度
混合推荐
推荐算法
collaborative filtering
Mass Diffusion （MD）
user activity
hybrid recommendation
recommendation algorithm
