基于偏好级别陌生人信息辅助的推荐系统

doi:10.3969/j.issn.1000-5641.2024.02.007

摘要/Abstract

摘要：

基于评论的推荐系统是一种主要通过挖掘文本信息抽取物品特征和用户偏好, 以提高性能的推荐系统方法. 现存的大多数方法忽略了撰写评论的陌生人信息, 引入陌生人信息可以更准确地衡量用户的相对感受并对目标用户的表达进行补充, 从而进行更精细的用户建模. 近年来, 一些研究尝试整合相似陌生人的信息, 但忽略了对其他陌生人信息的利用. 提出了基于偏好级别陌生人信息辅助的推荐系统模型CSRR(collaborative stranger review-based recommendation), 利用陌生人信息, 更加准确地对用户建模并进行适当扩展, 提升了推荐性能. 具体地, 为了准确捕捉用户的偏好, 首先, 设计了一个基于陌生人信息辅助的注意力模块, 该模块不仅考虑了评论文本的相似性, 也考虑了目标用户与撰写评论的陌生人之间的偏好交互作用; 其次, 一个基于陌生人信息过滤的门控模块根据目标用户–物品对的特征, 在偏好级别动态整合陌生人信息, 有效地过滤了陌生人的偏好信息以及丰富目标用户的建模; 最后, 应用隐因子模型 (latent factor model, LFM) 来完成评分预测任务. 实验结果说明CSRR模型在多个来源的真实数据集上均具有较高的预测准确度.

关键词: 基于评论的推荐系统, 陌生人信息, 注意力机制, 门控机制

Abstract:

Review-based recommendations are mainly based on the exploitation of textual information that reflects the characteristics of items and user preferences. However, most existing approaches overlook the influence of information from hidden strangers on the selection of reviews for the target user. However, information from strangers can more accurately measure the relative feelings of the user and provide a complement to the target user’s expression, leading to more refined user modeling. Recently, several studies have attempted to incorporate similar information from strangers but ignore the use of information regarding other strangers. In this study, we proposed a stranger collaborative review-based recommendation model to make effective use of information from strangers by improving accurate modeling and enriching user modeling. Specifically, for capturing potential user preferences elaborately, we first designed a collaborative stranger attention module considering the textual similarities and preference interactions between the target user and the hidden strangers implied by the reviews. We then developed a collaborative gating module to dynamically integrate information from strangers at the preference level based on the characteristics of the target user-item pair, effectively filtering preferences of strangers and enriching target user modeling. Finally, we applied a latent factor model to accomplish the recommendation task. Experimental results have demonstrated the superiority of our model compared to state-of-the-art methods on real-world datasets from various sources.

Key words: review-based recommendation, stranger information, attention mechanism, gating mechanism

中图分类号:

TP391

冯路平, 施力业, 吴雯, 郑骏, 胡文心, 郑巍. 基于偏好级别陌生人信息辅助的推荐系统[J]. 华东师范大学学报（自然科学版）, 2024, 2024(2): 53-64.

Luping FENG, Liye SHI, Wen WU, Jun ZHENG, Wenxin HU, Wei ZHENG. Collaborative stranger review-based recommendation[J]. Journal of East China Normal University(Natural Science), 2024, 2024(2): 53-64.

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数据集	用户数量/个	物品数量/个	评分数量/个	用户平均单词数/个	物品平均单词数/个	词汇表大小/个	评分密度/%
Office	4905	2420	53258	704.22	1427.41	47808	0.45
Toys	19412	11924	167597	447.29	727.85	63961	0.07
Tools	16638	10217	134476	435.50	709.10	68538	0.08
Yelp	1631	1633	78966	1991.19	1991.57	63107	2.96

类别	方法	MSE
类别	方法	Office	Toys	Tools	Yelp
基于评分的方法	NMF	0.9157	1.0960	1.2462	0.9850
基于评分的方法	SVD	0.7976	0.9179	1.0413	0.9321
基于陌生人的方法	PARL	0.7180	0.8349	0.9426	0.8815
基于陌生人的方法	MRMRP	0.7049	0.8299	0.9568	0.8809
基于评论的方法	DeepCoNN	0.7373	0.8545	0.9778	0.8728
	D-Attn	0.7090	0.8071	0.9783	0.8710
	MPCN	0.7110	0.8819	1.0033	0.8940
	NARRE	0.6992	0.7903	0.9423	0.8701
	DAML	0.7404	0.7895	0.9364	0.8789
	MRCP	0.6971	0.7978	0.9336	0.8663
本文方法	CSRR	0.6826*	0.7794*	0.9201*	0.8609*

指标	Office/%	Toys/%	Tools/%	Yelp/%
$\varDelta _{\rm{DAML}} $	7.80	1.28	1.74	2.04
$\varDelta _{\rm{MRCP}} $	2.08	2.31	1.45	0.62
$ {R}_{\overline {{\rm{AIP}}}} $	7.53	8.55	6.97	3.47

方法	MSE
方法	Office	Toys	Tools	Yelp
CSRR-Attn	0.6918	0.7814	0.9388	0.8653
CSRR-Gate	0.6878	0.7865	0.9261	0.8614
CSRR-ID	0.6843	0.8798	0.9519	0.8611
CSRR	0.6826	0.7794	0.9201	0.8609

方法	耗时/s
方法	Office	Toys	Tools	Yelp
CSRR-Attn	128	1093	305	290
CSRR-Gate	207	917	479	428
CSRR-ID	134	1160	308	300
CSRR	274	1286	699	551