分层级描述感知的个性化推荐系统

doi:10.3969/j.issn.1000-5641.2023.06.007

摘要/Abstract

摘要：

评论文本蕴含丰富的用户信息和物品信息, 对于用户的购买决策有着重要作用. 当用户面对不同的目标物品时, 会展现复杂的兴趣. 因此准确提取评论中的语义特征并建模物品和用户之间的上下文交互, 对学习用户偏好和物品属性十分关键. 专注于增强推荐系统的个性化捕捉和动态兴趣建模能力, 考虑不同特征的有用性, 提出了一种分层级描述感知的个性化推荐 (description-aware personal recommendation, DAPR) 算法: 在评论集合的单词层级, 设计个性化的信息选择网络, 提取重要的单词语义特征; 在评论层级, 基于交叉注意力机制设计神经网络, 动态地学习评论的有用性; 拼接评论摘要作为描述, 设计协同注意力网络, 捕捉更丰富的上下文感知的特征. 在5个Amazon数据集上的实验结果证明了所提方法能取得与基线模型相当的推荐性能.

关键词: 推荐系统, 基于评论, 注意力机制, 协同注意力

Abstract:

Review text contains comprehensive user and item information and it has a great influence on users’ purchase decision. When users interact with different target items, they may show complex interests. Therefore, accurately extracting review semantic features and modeling the contextual interaction between items and users is critical for learning user preferences and item attributes. Focusing on enhancing the personalization capture and dynamic interest modeling abilities of recommender systems, and considering the usefulness of different features, we propose a hierarchical description-aware personalized recommendation (DAPR) algorithm. At the word level of review text, we design a personalized information selection network to extract important word semantic features. At the review level, we design a neural network based on a cross-attention mechanism to dynamically learn the usefulness of reviews, concatenate review summaries as descriptions, and devise a co-attention network to capture rich context-aware features. The analysis of five Amazon datasets reveal that the proposed method can achieve comparable recommendation performance to the baseline models.

Key words: recommendation system, review-based, attention mechanism, co-attention

中图分类号:

TP391

陈道佳, 陈志云. 分层级描述感知的个性化推荐系统[J]. 华东师范大学学报（自然科学版）, 2023, 2023(6): 73-84.

Daojia CHEN, Zhiyun CHEN. Hierarchical description-aware personalized recommendation system[J]. Journal of East China Normal University(Natural Science), 2023, 2023(6): 73-84.

图/表 5

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参考文献 29

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数据集	评论数 (用户)/条	评论数 (物品)/条	评论长度	用户数量/个	物品数量/个	总评论数/条	评分密度/%
Digital Music	8	14	164	5541	3568	64706	0.327
Office Product	9	16	111	4905	2420	53258	0.449
Grocery and Food	8	10	78	14681	8713	151254	0.118
Toys and Games	7	11	77	19412	11924	167597	0.072
Video Games	7	15	147	24303	10672	231780	0.089

模型			MSE
模型	Digital Misic	Office Product	Grocery and Food	Toys and Games	Video Games
MF	0.8707	0.7149	1.0403	0.8808	1.2930
DeepCoNN	0.8109	0.7128	1.0223	0.8335	1.1950
D-Attn	0.8305	0.7103	1.0092	0.8334	1.2197
NARRE	0.8098	0.7064	0.9832	0.8025	1.1422
DAML	0.8102	0.7086	0.9836	0.8143	1.1722
MPCN	1.1192	0.8304	1.1549	0.9281	1.2610
DAPR-gate	0.8026	0.7059	0.9881	0.8055	1.1347
DAPR	0.7956	0.6956	0.9804	0.7967	1.1249

模型		MSE
模型	Digital Music	Toys and Games	Video Games
Base	0.8008	0.8144	1.1520
Base + Avg	0.8010	0.8162	1.1532
DAPR-WA	0.8095	0.8185	1.1467
DAPR-RA	0.7930	0.7998	1.1305
DAPR-Co	0.8124	0.8198	1.1424
DAPR	0.7956	0.7967	1.1249

模型		MSE
模型	Digital Music	Toys and Games	Video Games
DAPR-Dot	0.8236	0.8358	1.1625
DAPR-FM	0.8112	0.8100	1.1205
DAPR-LFM	0.7956	0.7967	1.1249

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