多指标推荐的全局邻域模型

吕红亮 1; 2; 王劲林 2; 邓峰 2

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多指标推荐的全局邻域模型

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- 多指标推荐的全局邻域模型
- A Global Neighborhood-Based Model with Multi-Criteria Recommendation
- 西安交通大学学报 2012年46卷第11期页码：98-105
- 作者机构：
  
  1. 中国科学院研究生院,北京,100049
  2. 中国科学院声学研究所国家网络新媒体工程技术研究中心,北京,100190
- 作者简介：
- 基金信息：
  
  : 2012-02-20. 作者简介: 吕红亮(1983-),男,博士生;王劲林(通信作者),男,研究员. 基金项目: 国家“863计划”重点资助项目(2011AA01A102);国家科技支撑计划资助项目(2011BAH11B04);中国科学院战略性先导科技专项资助项目(XDA06010302)
- DOI：
  中图分类号： TP393
- 网络首发：2012-11-10，
  
  纸质出版：2012
- 稿件说明：
移动端阅览
吕红亮 1, 2, 王劲林 2, 等. 多指标推荐的全局邻域模型[J]. 西安交通大学学报, 2012,46(11):98-105.

A Global Neighborhood-Based Model with Multi-Criteria Recommendation[J]. 2012, 46(11): 98-105.
吕红亮 1, 2, 王劲林 2, 等. 多指标推荐的全局邻域模型[J]. 西安交通大学学报, 2012,46(11):98-105. DOI：

A Global Neighborhood-Based Model with Multi-Criteria Recommendation[J]. 2012, 46(11): 98-105. DOI：

摘要

针对现有的多指标推荐模型预测精度较低、速度较慢的问题

提出一种多指标推荐的全局邻域模型(MGNgbr).该模型综合全局的打分信息和隐性反馈数据

通过随机梯度下降法学习物品在各个指标上的相似度

选择相似度最高的前k个邻居参与运算并最终预测用户对物品的打分信息.该模型具有预测准确度高、解释性好、计算复杂度低等优点.实验结果表明

该模型的预测准确度和分类准确度均优于现有的平均值融合模型、多维距离模型和多维奇异值分解模型

与多维奇异值分解模型相比

该模型还具有收敛快、运行时间短等优点.

Abstract

A global neighborhood-based model with multi-criteria recommendation(MGNgbr)is presented to improve the prediction precision and speed of current models. The model synthesizes the global rating information and implicit feedback data

considers the interrelation of criteria

uses the stochastic gradient descent method to learn the similarity of the items on all the criteria

and selects the most k similar neighbors for prediction. MGNgbr has high prediction accuracy with low computational complexity

and good explanatoriness. Experimental results show that MGNgbr produces better predictive accuracy and classification accuracy than the average similarity k-nearest neighbor model(Avg-KNN)

the multi-dimension distance model(M-Dist)and multi-dimension singular value decomposition model(MSVD)do. Comparison with the MSVD model shows that the proposed model also has characteristics of fast convergence and short running time.

关键词

Keywords

references

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