• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2016, Vol. 38 ›› Issue (01): 183-187.

• 论文 • 上一篇    下一篇

一种基于整体多样性增强的推荐算法

王森   

  1. (重庆理工大学计算机科学与工程学院,重庆 400054)
  • 收稿日期:2015-01-07 修回日期:2015-03-30 出版日期:2016-01-25 发布日期:2016-01-25
  • 基金资助:

    国家自然科学基金(61173040)

A recommendation algorithm
based on aggregate diversity enhancement  

WANG Sen   

  1. (College of Computer Science and Engineering,Chongqing University of Technology,Chongqing 400054,China)
  • Received:2015-01-07 Revised:2015-03-30 Online:2016-01-25 Published:2016-01-25

摘要:

目前大多数推荐算法都是以提高用户对未知商品的预测评分值为主要目标,然而预测准确率并不是增加用户满意度的唯一标准,推荐列表的多样性也是衡量推荐质量的一个重要指标。提出了一种新的推荐方法,旨在提高系统的整体多样性和长尾商品的推荐率。算法综合考虑了商品预测值、商品流行度、商品的偏爱度等多个标准。实验表明,与其他方法相比,本方法在维持较高推荐准确率的同时,能够推荐更多的长尾商品,提高了系统的整体多样性。关键词:

关键词: 推荐系统, 多样性, 覆盖率

Abstract:

The major objective of recommendation algorithms is to accurately predict the rating value of items.However, it has been recognized that accurate prediction of rating values is not the only requirement for achieving users’ satisfaction. The diversity of recommendation lists has gained importance recently. In this paper, we propose a novel recommendation algorithm which focuses on longtail item promotion and aggregate diversity enhancement. We take predicted item values, item popularity, item preference into account. Experimental results show that this approach can discover more worthrecommending long tails items, and improve aggregate diversity while maintaining a certain level of accuracy.

Key words: recommendation system;diversity;coverage