新疆大学学报（自然科学版中英文）2026，Vol.43Issue(2)：156-168,13.DOI:10.13568/j.cnki.651094.651316.2025.01.18.0001
结合对比学习的细粒度长短期偏好序列推荐
Fine-Grained Long and Short-Term Preference Sequential Recommendation with Contrastive Learning
摘要
Abstract
Sequence recommendation aims at item recommendation using users'long and short-term preferences,but most sequence recommendation systems face problems such as insufficient learning power and inadequate fusion of long and short-term preferences.Aiming at the above problems,this paper proposes a fine-grained long and short-term preference sequence recommendation method based on contrastive learning.1)To address the problem of insufficient long and short-term prefer-ence fusion,this paper proposes a long and short-term preference learning layer and a long and short-term preference fusion layer.Firstly,it splits the user behaviour sequence into multi-period sessions and extracts the user's short-term preference in each session by using gated recurrent units,and then fuses the short-term preference sequences to capture the user's long-term preference through the multi-head attention mechanism.Finally,the long-term and short-term preferences are fused adap-tively based on the time span to obtain a more representative and comprehensive preference representation.2)Aiming at the problem of insufficient learning power due to data sparsity,a preference representation comparison learning task is designed to introduce agent user preferences for comparison learning to achieve more accurate preference recommendation.The experi-mental results show that:compared to the sub-optimal methods,the model improves the Hit@20 metric by 9.84%,6.40%,and 1.52%,and the MAP@20 metric by 22.64%,2.42%,and 6.42%on three public datasets,respectively,demonstrating the effec-tiveness of the proposed method.关键词
推荐系统/序列推荐/对比学习/自注意力机制/门控循环单元Key words
recommender system/sequential recommendation/contrastive learning/self-attention mechanism/gated recur-rent unit分类
信息技术与安全科学引用本文复制引用
杨兴耀,武彦孚,张祖莲,于炯,钟志强,陈羽..结合对比学习的细粒度长短期偏好序列推荐[J].新疆大学学报（自然科学版中英文）,2026,43(2):156-168,13.基金项目
新疆维吾尔自治区自然科学基金面上项目"基于知识图谱与图神经网络的信息聚合及特征表示推荐技术研究"(2023D01C17),"气温预报误差的地形依赖性与南疆高山区夏季高温智能网格预报技术研究"(2023D01A123) （2023D01C17）
国家自然科学基金"大数据流式计算环境下基于预测的资源调度性能优化研究"(62262064) （62262064）
新疆维吾尔自治区科技计划项目-天山创新团队计划"面向农业的天地协同水资源时空精准调度研究及应用创新团队"(2023D4012). （2023D4012）
