摘要
结合信任的推荐系统可以有效地缓解传统协同过滤算法中存在的数据稀疏问题,并能给每个用户提供可信且准确的推荐。然而系统中的每个用户都是不同的,因此考虑针对不同用户应采用不同推荐模式来查找推荐群体,以做出更具个性化的推荐。研究了微观层次上的节点特性,引入了兴趣的概念,证明了被推荐者的多种节点特性对于推荐结果的影响效果。最后通过多组实验验证了推荐系统在具有不同特性的节点上的推荐效果差异。
The data sparseness is due to the nature of traditional collaborative filtering and trust-based recommender systems can effectively deal with the sparse data without losing accuracy. It is appropriate to use different methods for different users to give more personalized recommendation. The vertex characteristic in microcosmic stratums was studied, and the formal definition of interest was proposed. It was used to demonstrate the impact of local structures of the recommended user on the results of recommender systems. In the end, several results were given to illustrate the diversity of the effects of recommender systems on users of different types.
出处
《计算机应用》
CSCD
北大核心
2014年第1期222-226,235,共6页
journal of Computer Applications
基金
国家自然科学基金资助项目(61170224)
山东省自然科学基金资助项目(ZR2011FL018
ZR2012FL07)
山东省科技发展计划项目(2012GGB01017)
山东省教育厅科技计划项目(J13LN24
J11LG35
J10LG27)
烟台大学青年基金资助项目(JS11Z8)
关键词
信任
推荐系统
局部网络结构
兴趣
个性化推荐
trust
recommender system
local network structure
interest
personalized recommendation