融合偏好学习和意图建模的个性化服装序列推荐

doi:10.19678/j.issn.1000-3428.0069819

计算机工程 ›› 2025, Vol. 51 ›› Issue (12): 130-139. doi: 10.19678/j.issn.1000-3428.0069819

融合偏好学习和意图建模的个性化服装序列推荐

田志轩¹, 刘骊¹^,²^,*(), 付晓东¹^,², 刘利军¹^,², 彭玮¹^,²

1. 昆明理工大学信息工程与自动化学院, 云南昆明 650500
2. 昆明理工大学云南省计算机技术应用重点实验室, 云南昆明 650500

收稿日期:2024-05-07 修回日期:2024-06-23 出版日期:2025-12-15 发布日期:2024-09-02
通讯作者: 刘骊
基金资助:
国家自然科学基金(62262036); 云南省兴滇英才支持计划(KKRD201903025)

Personalized Clothing Sequence Recommendation Fused with Preference Learning and Intention Modeling

TIAN Zhixuan¹, LIU Li¹^,²^,*(), FU Xiaodong¹^,², LIU Lijun¹^,², PENG Wei¹^,²

1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
2. Computer Technology Application Key Laboratory of Yunnan Province, Kunming University of Science and Technology, Kunming 650500, Yunnan, China

Received:2024-05-07 Revised:2024-06-23 Online:2025-12-15 Published:2024-09-02
Contact: LIU Li

摘要/Abstract

摘要：

针对现有个性化服装序列推荐方法中用户偏好动态变化、用户意图难以度量以及用户需求与服装交互序列间存在空间异构性的问题, 提出一种融合偏好学习和意图建模的个性化服装序列推荐方法。首先对输入的用户-服装交互序列进行偏好提取, 得到用户-服装的长期偏好、短期偏好及主观偏好; 其次构建偏好学习模块, 自动度量偏好权重, 得到动态偏好; 然后引入知识库和注意力机制, 通过定义交互意图集, 对当前用户的主观偏好进行意图建模, 得到意图偏好; 最后定义统一偏好空间, 对动态偏好和意图偏好进行偏好融合, 输出最终用户-服装的总体偏好并将其用于个性化服装序列推荐。在公开数据集iFashion与Amazon上的实验结果表明, 所提方法的召回率分别为0.699 1和0.370 3, 较对比方法平均提升了27.80%和20.53%。

Abstract:

To address the dynamic changes in user preference, difficulty in measuring user intention, and spatial heterogeneity between user demand and clothing interaction sequences, this study proposes a personalized sequential clothing recommendation method fused with preference learning and intention modeling. First, preferences are extracted from input user-clothing interaction sequences to obtain long-term, short-term, and subjective preferences. Then, a preference learning module is constructed, and preference weights are measured automatically to obtain dynamic preferences. Subsequently, the knowledge base and attention mechanism are introduced, and the intention preferences of current users are modeled by defining the interaction intention set based on the users' subjective preferences. Finally, a unified preference space is defined, and preference fusion is performed on dynamic and intention preferences to output the global user-clothing preference, which is used for personalized clothing sequence recommendation. Experimental results on the open datasets iFashion and Amazon show that the proposed method achieves recall rates of 0.699 1 and 0.370 3, which are 27.80% and 20.53% higher than those of the comparative methods, respectively.

Key words: personalized clothing sequence recommendation, preference extraction, preference learning, intention modeling, preference fusion

田志轩, 刘骊, 付晓东, 刘利军, 彭玮. 融合偏好学习和意图建模的个性化服装序列推荐[J]. 计算机工程, 2025, 51(12): 130-139.

TIAN Zhixuan, LIU Li, FU Xiaodong, LIU Lijun, PENG Wei. Personalized Clothing Sequence Recommendation Fused with Preference Learning and Intention Modeling[J]. Computer Engineering, 2025, 51(12): 130-139.

https://www.ecice06.com/CN/Y2025/V51/I12/130

图/表 6

图1 个性化服装序列推荐流程图

Fig.1 Flowchart of personalized clothing sequence recommendation

图2 Top-10推荐结果的可视化示例

Fig.2 Visual examples of Top-10 recommendation results

图3 超参数N、φ和γ对推荐性能的影响

Fig.3 Influence of hyperparameters N, φ, γ on recommendation performance

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