计算机工程 ›› 2018, Vol. 44 ›› Issue (9): 64-69.doi: 10.19678/j.issn.1000-3428.0048497

• 先进计算与数据处理 • 上一篇    下一篇

融合多数据源的动态自适应推荐算法

陈晓霞,卢菁   

  1. 上海理工大学 光电信息与计算机工程学院,上海 200093
  • 收稿日期:2017-09-01 出版日期:2018-09-15 发布日期:2018-09-15
  • 作者简介:陈晓霞(1992—),女,硕士研究生,主研方向为社交网络、数据挖掘;卢菁,讲师、博士。
  • 基金项目:

    上海理工大学科技发展项目(16KJFZ039)。

Dynamic Adaptive Recommendation Algorithm Fusing Multiple Data Sources

CHEN Xiaoxia,LU Jing   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Received:2017-09-01 Online:2018-09-15 Published:2018-09-15

摘要:

现有推荐系统的研究多数基于单一数据源、单一推荐算法或简单加性融合,忽略了数据源及算法动态融合的重要性,导致推荐精确度不高。为解决该问题,提出一种新的动态自适应推荐算法。利用基础数据求出艺术家流行度和时间衰减因子,将其作为推荐数据源,降低由数据 源单一导致的推荐误差。通过权重因子集成基于邻域方法和矩阵分解技术构建组合模型。将数据源应用于模型,运用固定步长的权重因子调整2种方法在模型中的占比,根据推荐结果的召回率实现动态自适应调整。在真实数据集上的实验结果表明,与简单加性融合、FSWA算 法相比,该算法具有较高的推荐精确度。

关键词: 音乐推荐, 多数据源, 推荐系统, 自适应融合, 动态调整

Abstract:

Most of the researches of recommendation system are based on single data source,single recommendation algorithm or simple additive fusion,ignoring the importance of data source and dynamic fusion of algorithms,resulting in low recommendation accuracy.To solve this problem,a new dynamic adaptive recommendation algorithm is proposed.The basic data is used to find the artist popularity and time decay factor,which is used as the recommendation data source to reduce the recommendation error caused by the single data source.A more accurate combination model is constructed by weight factor integration based on neighborhood method and matrix decomposition technology.The data source is applied to the model,and the weight factor of the fixed step is used to adjust the proportion of the two methods in the model,and the dynamic adaptive adjustment is realized according to the recall rate of the recommendation result.Experimental results on the real data set show that this algorithm has higher recommendation accuracy than simple additive fusion and FSWA algorithm.

Key words: music recommendation, multiple data sources, recommendation system, adaptive fusion, dynamic adjustment

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