Long- and Short-Term Preference Model Based on Graph Embedding for Sequential Recommendation

被引：4

作者：

Liu, Yu ^{[1
,2
]}

Zhu, Haiping ^{[1
,2
]}

Chen, Yan ^{[1
,2
]}

Tian, Feng ^{[1
,2
]}

Ma, Dailusi ^{[1
,2
]}

Zeng, Jiangwei ^{[1
,2
]}

Zheng, Qinghua ^{[1
,2
]}

机构：

[1] Xi An Jiao Tong Univ, Sch Comp Sci & Technol, Xian, Peoples R China

[2] Xi An Jiao Tong Univ, Shaanxi Prov Key Lab Satellite & Terr Network Tec, Xian, Peoples R China

来源：

DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, DASFAA 2020 | 2020年 / 12115卷

基金：

中国国家自然科学基金;

关键词：

Long-termshort-term preference; Sequential recommendation; Graph embedding; Attention networks;

D O I：

10.1007/978-3-030-59413-8_20

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

As sequential recommendation mainly obtains the user preference by analyzing their transactional behavior patterns to recommend the next item, how to mine real preference from user's sequential behavior is crucial in sequential recommendation, and how to find the user long-term and short-term preference accurately is the key to solve this problem. Existing models mainly consider either the user short-term preference or long-term preference, or the relationship between items in one session, ignoring the complex item relationships between different sessions. As a result, they may not adequately reflect the user preference. To this end, in this paper, a Long- and Short-Term Preference Network (LSPN) based on graph embedding for sequential recommendation is proposed. Specifically, item embedding with a complex relationship of items between different sessions is obtained based on graph embedding. Then this paper constructs the network to obtain the user long- and short-term preferences separately, combing them through the fuzzy gate mechanism to provide the user final preference. Furthermore, the results of experiments on two datasets demonstrate the efficiency of our model in Recall@N and MRR@N.

引用

页码：241 / 257

页数：17

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