计算机应用 ›› 2014, Vol. 34 ›› Issue (1): 218-221.DOI: 10.11772/j.issn.1001-9081.2014.01.0218

• 人工智能 • 上一篇    下一篇

融合推荐潜力的个性化趋势预测的混合推荐模型

陈洪涛,肖如良,倪友聪,杜欣,龚平,蔡声镇   

  1. 福建师范大学 软件学院,福州 350108
  • 收稿日期:2013-07-02 修回日期:2013-08-31 出版日期:2014-01-01 发布日期:2014-02-14
  • 通讯作者: 肖如良
  • 作者简介:陈洪涛(1989-),男,湖北咸宁人,硕士研究生,主要研究方向:推荐系统、机器学习;肖如良(1966-),男,湖南娄底人,教授,博士,CCF会员,主要研究方向:Web智能、云计算、物联网;倪友聪(1975-),男,安徽合肥人,讲师,博士,CCF会员,主要研究方向:软件体系结构、虚拟化、云计算;杜欣(1979-),女,新疆石河子人,副教授,博士,CCF会员,主要研究方向:演化计算、模式识别;龚平(1982-),男,福建三明人,讲师,博士,主要研究方向:软件体系结构、虚拟化、云计算;蔡声镇(1954-),男,福建晋江人,教授,主要研究方向:嵌入式系统、信息系统。
  • 基金资助:

    教育部规划基金资助项目;福建省科技计划重大项目

Hybrid recommendation model for personalized trend prediction of fused recommendation potential

CHEN Hongtao,XIAO Ruliang,NI Youcong,DU Xin,GONG Ping,CAI Sheng-zhen   

  1. School of Software, Fujian Normal University, Fuzhou Fujian 350108, China
  • Received:2013-07-02 Revised:2013-08-31 Online:2014-01-01 Published:2014-02-14
  • Contact: XIAO Ruliang

摘要: 预测用户对物品的行为中,准确的物品推荐是推荐系统的困难问题。为了提高推荐系统的推荐精度,引入物品的推荐潜力,提出一种新颖的融合物品推荐潜力的个性化混合推荐模型。首先根据最近短时间段和最近长时间段的物品访问率计算趋势动量,然后利用趋势动量计算出当前物品的推荐潜力值,最后将物品推荐潜力值融入到个性化推荐模型中得到混合推荐模型。实验证明,融合了物品推荐潜力值的个性化趋势预测,能较大地提高推荐系统的推荐精度。

关键词: 推荐系统, 混合推荐, 推荐潜力, 个性化, 趋势预测

Abstract: In recommendation system, it is difficult to predict the behavior of users on items and give the accurate recommendation. In order to improve the accuracy of recommendation system, the recommendation potential was introduced and a novel personalized hybrid recommendation model fused with recommendation potential was proposed. Firstly, the trend momentum was calculated according to the visits of items in recent short time and long time; then, the current recommendation potential was calculated utilizing trend momentum; finally, the hybrid recommendation model was achieved according to the fusion of recommendation potential and personalized recommendation model. The experimental results show that the personalized trend prediction fused with recommendation potential can improve the accuracy of recommendation system in a large scale.

Key words: recommendation system, hybrid recommendation, recommendation potential, personalization, trend prediction

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