计算机工程 ›› 2012, Vol. 38 ›› Issue (23): 63-66.doi: 10.3969/j.issn.1000-3428.2012.23.015

• 软件技术与数据库 • 上一篇    下一篇

基于记忆效应的协同过滤推荐算法

杨福萍1,2,王洪国1,董树霞3,赵学臣1,2   

  1. (1. 山东师范大学信息科学与工程学院,济南 250014; 2. 山东省分布式计算机软件新技术重点实验室,济南 250014;3. 山东女子学院,济南 250300)
  • 收稿日期:2012-02-16 出版日期:2012-12-05 发布日期:2012-12-03
  • 作者简介:杨福萍(1986-),女,硕士、CCF会员,主研方向:数据挖掘;王洪国,教授、博士生导师;董树霞,讲师、硕士;赵学臣,硕士研究生
  • 基金项目:
    山东省自然科学基金资助项目(ZR2011FQ029; ZR2011FL026);山东省科技发展计划基金资助项目(2011YD01099)

针对传统协同过滤算法无法及时反映用户兴趣变化的情况,将人脑的记忆和遗忘特性引入到个性化推荐中,提出基于记忆效应的协同过滤推荐算法。利用短时记忆体现用户近期兴趣变化,应用长时记忆强调用户早期兴趣的重要性,给出将短时记忆和长时记忆相结合的调和记忆,使推荐系统可以自适应地跟踪用户兴趣变化。实验结果表明,与CF算法、SCF算法和AUICF算法相比,该算法的推荐精度更高、收敛速度更快。

YANG Fu-ping 1,2, WANG Hong-guo 1, DONG Shu-xia 3, ZHAO Xue-chen 1,2   

  1. (1. School of Information Science & Engineering, Shandong Normal University, Jinan 250014, China; 2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan 250014, China; 3. Shandong Women’s University, Jinan 250300, China)
  • Received:2012-02-16 Online:2012-12-05 Published:2012-12-03

摘要: 针对传统协同过滤算法无法及时反映用户兴趣变化的情况,将人脑的记忆和遗忘特性引入到个性化推荐中,提出基于记忆效应的协同过滤推荐算法。利用短时记忆体现用户近期兴趣变化,应用长时记忆强调用户早期兴趣的重要性,给出将短时记忆和长时记忆相结合的调和记忆,使推荐系统可以自适应地跟踪用户兴趣变化。实验结果表明,与CF算法、SCF算法和AUICF算法相比,该算法的推荐精度更高、收敛速度更快。

关键词: 协同过滤, 记忆效应, 记忆元, 兴趣偏好, 个性化推荐

Abstract: Existing collaborative filtering algorithms can not promptly reflect the change of users’ interest. For this reason, this paper introduces the human brain’s characteristics of memory and forgetting to personalized recommendation, and proposes a collaborative filtering algorithm based on memory. The effective use of short-term memory reflects users’ recent interest. Long-term memory emphasizes the importance of users’ early interest. At the same time, it combines the short-term memory with the long-term memory and proposes the reconciled memory, which makes the recommender system adaptively track the change of users’ interest. Experimental results show that the proposed algorithm has high quality of precision and rapid convergence rate and that it overcomes the low efficiency of CF, SCF, AUICF algorithms to some extent.

Key words: collaborative filtering, memory effect, Memory Cell(MC), interest preference, personality recommendation

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