SeqRec：基于长期偏好和即时兴趣的序列推荐模型

doi:10.3785/j.issn.1008-973X.2020.06.015

浙江大学学报(工学版)

2020, Vol. 54

Issue (6): 1177-1184 DOI: 10.3785/j.issn.1008-973X.2020.06.015

计算机技术

SeqRec：基于长期偏好和即时兴趣的序列推荐模型

张岩(

),郭斌*(

),王倩茹,张靖,於志文

西北工业大学计算机学院，陕西西安 710072

SeqRec: sequential-based recommendation model with long-term preference and instant interest

Yan ZHANG(

),Bin GUO*(

),Qian-ru WANG,Jing ZHANG,Zhi-wen YU

School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China

全文: PDF(767 KB) HTML

摘要：

通过对用户的历史购物序列进行建模，得到用户稳定的长期偏好和动态的即时兴趣，并聚合长期偏好和即时兴趣进行个性化推荐. 提取用户对商品的评论内容用于表示商品的特征；使用递归神经网络从用户的历史购买序列数据中学习用户稳定的长期偏好，使用提问数据对用户不断变化的即时兴趣进行建模；通过注意力机制为长期偏好以及即时兴趣分配不同的权重，得到用户最终偏好的向量表示. 在亚马逊真实数据集上的实验结果表明，SeqRec模型在召回率和精确率上比当前主流的序列推荐方法至少超出10%；SeqRec模型证明不同用户的长期偏好和即时兴趣对其下次购买物品的影响程度不同.

关键词： 序列数据; 个性化推荐; 长期偏好; 即时兴趣; 注意力机制

Abstract:

The user’s stable long-term preferences and dynamic instant interests were obtained by modeling on the user’s historical behavior records, and the user preferences were aggregated for personalized recommendation. Firstly, the users’ reviews on the items were extracted to represent the characteristics of the items. Secondly, users’ historical behavior records were used to represent their stable long-term preferences, and query data was used to model their instant interests. Third, the user’s final preferences were aggregated by assigning different weights to the long-term preferences and instant interests through the attention mechanism. Experiments on real data sets of Amazon were conducted to evaluate the performance of SeqRec model, and results show that it is superior to the current state-of-the-art sequential recommendation methods more than 10% in recall rate and percision ratio. Meanwhile, SeqRec model proves that the long-term preferences and instant interests of different users have different influences on their next purchases.

Key words: sequential data personalized recommendation long-term preferences instant interests attention mechanism

收稿日期: 2020-01-02 出版日期: 2020-07-06

CLC:

TU 111

通讯作者: 郭斌 E-mail: 1065120019@qq.com;guob@nwpu.edu.cn

作者简介: 张岩（1996—），男，硕士生，从事推荐算法研究. orcid.org/0000-0003-0806-2006. E-mail： 1065120019@qq.com

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引用本文:

张岩,郭斌,王倩茹,张靖,於志文. SeqRec：基于长期偏好和即时兴趣的序列推荐模型[J]. 浙江大学学报(工学版), 2020, 54(6): 1177-1184.

Yan ZHANG,Bin GUO,Qian-ru WANG,Jing ZHANG,Zhi-wen YU. SeqRec: sequential-based recommendation model with long-term preference and instant interest. Journal of ZheJiang University (Engineering Science), 2020, 54(6): 1177-1184.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.06.015 或 http://www.zjujournals.com/eng/CN/Y2020/V54/I6/1177

图 1 基于长期偏好和即时兴趣的序列推荐（SeqRec）模型框架图

表 1 电子产品和婴儿用品数据集统计信息

表 2 SeqRec模型与对比模型的召回率和命中率结果对比

图 2 不同用户对长期偏好和即时兴趣的权重

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