摘要
传统的基于图的推荐算法忽略了时间综合信息影响从而导致推荐质量不高。针对这一问题,提出一种融合时间综合影响的轮盘赌游走个性化推荐算法。该算法以用户-项目二分图为基础,引入衰减函数,将时间综合信息对推荐的影响量化成图节点的关联概率;然后采用轮盘赌模型根据关联概率选择游走目标;最终对每个用户做出top-N推荐。实验结果表明:该算法比传统基于图的随机游走PersonalRank算法在推荐的准确度、召回率以及覆盖率指标上都有明显提高。
The traditional graph-based recommendation algorithm neglects the combined time factor which results in the poor recommendation quality.In order to solve this problem,a personalized recommendation algorithm integrating roulette walk and combined time effect was proposed.Based on the user-item bipartite graph,the algorithm introduced attenuation function to quantize combined time factor as association probability of the nodes; Then roulette selection model was utilized to select the next target node according to those associated probability of the nodes skillfully; Finally,the top-N recommendation for each user was provided.The experimental results show that the improved algorithm is better in terms of precision,recall and coverage index,compared with the conventional PersonalRank random-walk algorithm.
出处
《计算机应用》
CSCD
北大核心
2014年第4期1114-1117,1129,共5页
journal of Computer Applications
基金
教育部规划基金资助项目(11YJA860028)
福建省自然科学基金资助项目(2013J01219)
