计算机应用 ›› 2010, Vol. 30 ›› Issue (05): 1287-1289.

• 数据挖掘与人工智能 • 上一篇    下一篇

基于增量学习的混合推荐算法

任磊   

  1. 上海师范大学
  • 收稿日期:2009-10-25 修回日期:2009-12-04 发布日期:2010-05-04 出版日期:2010-05-01
  • 通讯作者: 任磊
  • 基金资助:
    上海市科委重大科技攻关计划项目;上海师范大学理工科项目

Hybrid recommendation approach based on incremental learning

REN Lei   

  • Received:2009-10-25 Revised:2009-12-04 Online:2010-05-04 Published:2010-05-01
  • Contact: REN Lei

摘要: 推荐系统是自适应信息系统中的个性化服务模块,可以根据目标用户的信息需求提供个性化的信息服务。针对传统协作过滤算法存在的用户兴趣描述粒度过大问题,以及稀疏评分矩阵造成相似度计算不准确的问题,提出了一种基于增量学习的混合推荐算法WHHR,该算法通过Widrow-Hoff增量学习构建基于内容的用户模型,并结合协作过滤推荐机制实现评分预测。实验验证了WHHR算法在收敛速度和推荐准确性方面较类似推荐算法有较大提高。

关键词: 混合推荐算法, 增量学习, 用户建模, 基于内容的过滤, 协作过滤

Abstract: Recommendation is a kind of personalized service in the adaptive information system, and it can provide personalized information according to individual information needs. Concerning the issues of the user profiling granularity and the sparsity of user-item matrix in the classical collaborative filtering, a hybrid recommendation approach named WHHR was proposed based on incremental learning. WHHR implemented user modeling with Widrow-Hoff learning and predicted ratings by collaborative filtering. The experimental results demonstrate that the proposed approach outperforms other similar ones in convergence and accuracy.

Key words: hybrid recommender approach, incremental learning, user profiling, content-based filtering, collaborative filtering