计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

基于SVD++与标签的跨域推荐模型

邢长征,杨晓婷   

  1. (辽宁工程技术大学 电子与信息工程学院,辽宁 葫芦岛 125105)
  • 收稿日期:2017-02-09 出版日期:2018-04-15 发布日期:2018-04-15
  • 作者简介:邢长征(1967—),男,教授,主研方向为数据挖掘;杨晓婷(通信作者),硕士研究生。

Cross-domain Recommendation Model Based on SVD++ and Tag

XING Changzheng,YANG Xiaoting   

  1. (School of Electronics and Information Engineering,Liaoning Technical University,Huludao,Liaoning 125105,China)
  • Received:2017-02-09 Online:2018-04-15 Published:2018-04-15

摘要: 在现有多数跨域推荐模型中,用户不能给指定项目添加标签,并且建立模型时未考虑用户的历史标签,导致推荐误差变大。针对上述问题,构建基于SVD++模型并融合标签推荐的跨域推荐模型TagSVD++。该模型继承SVD++模型利用评分数据预测的特点,加入用户和项目标签信息,通过标签使用次数反映用户喜好和项目特征,并且引入热门惩罚系数避免热门标签和项目对推荐预测的干扰。在真实电影和图书网站相关数据模拟的跨领域数据集上进行实验,结果表明,TagSVD++模型能有效提高跨域推荐的准确性。

关键词: 跨域推荐, 热门惩罚系数, 标签推荐, SVD++模型, 推荐模型

Abstract: Currently,most cross-domain recommendation models have two problems:the prediction error becomes large when the user fails to add a tag to the specified item and when building the model,the user’s history label is not taken into account,which causes the decreasing of recommendation result’s accuracy.In order to solve these two problems,a cross-domain recommendation model is constructed,named TagSVD++,which is based on the SVD++ model and fused with the tag recommendation.This model not only inherits the characteristics of SVD++ model’s using of score data to predict,but also adds the user and the project label information.It uses the tag’s frequency to reflect user preferences and characteristics,and introduces the popular penalty factor to prevent hot tags and popular items from bringing interference to recommendation and predictions.Comparative tests on cross-domain data sets for data simulation which is related to real movie and book website are conducted.Experimental results show that TagSVD++ model can improve the accuracy of cross-domain recommendations effectively.

Key words: cross-domain recommendation, hot punish coefficient, tag recommendation, SVD++ model, recommendation model

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