融合用户感知和多因素的兴趣点推荐

doi:10.3785/j.issn.1008-973X.2023.02.011

浙江大学学报(工学版)

2023, Vol. 57

Issue (2): 310-319 DOI: 10.3785/j.issn.1008-973X.2023.02.011

计算机技术

融合用户感知和多因素的兴趣点推荐

卢巧杰1,2(

),王楠1,2,*(

),李金宝3,4,李坤1,2

1. 黑龙江大学计算机科学技术学院，黑龙江哈尔滨 150080
2. 黑龙江大学数据库与并行计算重点实验室，黑龙江哈尔滨 150080
3. 齐鲁工业大学山东省人工智能研究院，山东济南 250014
4. 齐鲁工业大学数学与统计学院，山东济南 250353

Point-of-interest recommendation integrating user perception and multi-factor

Qiao-jie LU1,2(

),Nan WANG1,2,*(

),Jin-bao LI3,4,Kun LI1,2

1. College of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
2. Key Laboratory of Database and Parallel Computing, Heilongjiang University, Harbin 150080, China
3. Shandong Artificial Intelligence Institute, Qilu University of Technology, Jinan 250014, China
4. School of Mathematics and Statistics, Qilu University of Technology, Jinan 250353, China

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摘要：

针对传统的基于协同过滤的兴趣点(POI)推荐方法存在数据稀疏问题和现有工作往往单纯利用上下文信息却没有合理平衡各因素的作用影响的问题，提出融合用户感知和多因素的兴趣点推荐模型（UPMF）. 为基于用户感知的隐式建模提取用户相似性以丰富用户表示，并利用序列、地理和社交等上下文信息构建用户感知协同影响的POI推荐模型，缓解数据稀疏问题. 设计新颖的用户感知的融合策略(UPIS)，在基于用户感知的同时合理利用各种上下文信息挖掘用户的动态偏好. 提出基于分段的活动区域选择算法针对不同活动区域对用户的影响进行建模. 实验结果表明，与其他流行的POI推荐方法相比，UPMF在准确率、召回率和归一化折损累计增益 (NDCG) 3 个评价标准上都有一定程度的提高. 在Gowalla和Yelp数据集上，UPMF模型的NDCG@10比SUCP的分别高12.77%、7.24%.

关键词： 社交网络; 兴趣点(POI)推荐; 用户感知; 隐式反馈; 多因素

Abstract:

Traditional collaborative filtering-based point-of-interest (POI) recommendation methods suffer from data sparsity and existing work tends to use contextual information alone without a reasonable balance of the effect of each factor. A point-of-interest recommendation model integrating user perception and multi-factor (UPMF) was proposed. User similarities for user perception-based implicit modelling were extracted to enrich user representations, and contextual information such as sequence, geography and social was used to construct POI recommendation models with synergistic user perception influences, which alleviated data sparsity. A novel user perception integration strategy (UPIS) was designed to mine the dynamic preferences of users based on user perceptions while making reasonable use of various contextual information. In addition, a segmentation-based activity area selection algorithm was proposed to model the impact of different activity areas on users. Results show that UPMF can improve the performance on metrics of precision, recall and normalized discounted cumulative gain (NDCG) compared to other popular POI recommendation technologies. Specifically, UPMF outperforms SUCP by 12.77% and 7.24% on the Gowalla and Yelp datasets in terms of NDCG@10, respectively.

Key words: social network point-of-interest (POI) recommendation user perception implicit feedback multi-factor

收稿日期: 2022-07-30 出版日期: 2023-02-28

CLC:

TP 391

基金资助: 国家重点研发计划资助项目（2020YFB1710200）；黑龙江省自然科学基金资助项目（LH2021F047）

通讯作者: 王楠 E-mail: wlqj0507@163.com;wangnan@hlju.edu.cn

作者简介: 卢巧杰（1998—），女，硕士生，从事推荐系统研究. orcid.org/0000-0001-6584-398X. E-mail： wlqj0507@163.com

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引用本文:

卢巧杰,王楠,李金宝,李坤. 融合用户感知和多因素的兴趣点推荐[J]. 浙江大学学报(工学版), 2023, 57(2): 310-319.

Qiao-jie LU,Nan WANG,Jin-bao LI,Kun LI. Point-of-interest recommendation integrating user perception and multi-factor. Journal of ZheJiang University (Engineering Science), 2023, 57(2): 310-319.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2023.02.011 或 https://www.zjujournals.com/eng/CN/Y2023/V57/I2/310

图 1 融合用户感知和多因素的兴趣点推荐（UPMF）模型框架图

表 1 数据集的统计信息

表 2 UPMF模型与基线对比模型在Gowalla和Yelp数据集上的P@N、R@N和NDCG@N性能比较

表 3 UPMF模型与其变体模型在Gowalla和Yelp数据集上的P@N、R@N和NDCG@N性能比较

图 2 不同的数据稀疏度在数据集Gowalla和Yelp上对Recall@20和NDCG@20的影响

图 3 用户感知的融合策略(UPIS)的不同参数的影响

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