摘要
近些年,推荐系统的结果多样性引起了人们的广泛关注。为了提高推荐结果的多样性,同时保证一定的推荐准确性,应用产品数据中的长尾数据项来提高推荐结果的多样性,提出了一种包含资源分配和推荐2个阶段的推荐算法。在资源分配中,将"被推荐的机会"视为资源,通过预定义的分配策略将这些资源分配给所有的项。在推荐阶段,按照每个用户对所有项的偏好情况计算每个用户所分配的资源,并基于资源的分布情况对用户进行推荐。最后,给出了一种用于平衡推荐准确性与多样性的权衡算法。实验表明,提出的推荐算法与相关推荐算法相比较能更好的应用长尾数据,在保证推荐结果准确性的同时大大提高了推荐结果的多样性。
Recently, the diversity of results in recommender system has attracted more and more people. In order to improve the diversity while maintaining reasonable accuracy of recommender results, by applying the long tail items of the product distribution, this paper proposed a two-stage algorithm including resource allocation and recommendation stages. In the resource allocation stage, we represented the chance of being recommended as resources, allocated the resources on all items according to predefmed allocation strategy. In the recommendation stage, we calculated the resources allocated to each user based on his or her preferences for all items, and made recommendation according to the distribution of resources. Finally, we proposed an algorithm that Waded off the diversity and accuracy of the results in recommender system. The experiments show that, compared with related works, the proposed recommender algorithm has higher diversity while maintaining reasonable accuracy using long tail data.
出处
《控制工程》
CSCD
北大核心
2015年第6期1137-1141,共5页
Control Engineering of China
基金
国家自然科学基金(61170102)
湖南省自然科学基金(2015JJ2046)
湖南省教育厅科研项目(13C030)
关键词
推荐系统
长尾数据
多样性
学习算法
Recommendation system
long tail data
diversity
learning algorithm
