基于粒子群优化和LightGBM的情景感知多式联运推荐
Context-Aware Multi-modal Transportation Recommendation Based on Particle Swarm Optimization and LightGBM
查看参考文献20篇
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文摘
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针对交通推荐服务中推荐的出行方式单一、忽略用户出行偏好以及多分类任务中样本类别不平衡等问题,本文提出一种基于粒子群优化和LightGBM的情景感知多式联运推荐方法.该方法综合考虑用户在时间、空间以及出行成本上的出行偏好,利用数理统计和表示学习方法捕捉用户出行与各要素之间的内在关系.同时,为了缓解样本类别不平衡带来的负面影响,利用基于粒子群优化算法的指标优化方法为每个类别搜索最优权重,对模型的预测结果进行修正,以实现最大化评价指标的目的.实验结果表明,与传统算法相比,本文提出的模型在时空特征提取、缓解类别不平衡和推荐准确性上均有较好的表现. |
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其他语种文摘
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In order to solve the problems of considering only one transportation mode and neglecting user preference in transportation recommendation problem,and class imbalance problem in multi-class task,a context-aware multi-modal transportation recommendation method based on particle swarm optimization and LightGBM is proposed. This method comprehensively considers the user’s travel preferences in terms of time, space and travel cost, and uses mathematical statistics and representation learning methods to capture the internal relationship between user travel and various elements. At the same time, in order to alleviate the negative impact caused by the imbalance of sample class, the index optimization method based on particle swarm optimization algorithm is used to search for the optimal weight for each class, and the prediction results of the model are modified to achieve the purpose of maximizing the evaluation index. Experimental results show that compared with traditional algorithms, the model proposed in this paper has better performance in spatio-temporal feature extraction, alleviating class imbalance and recommendation accuracy. |
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来源
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电子学报
,2021,49(5):894-903 【核心库】
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DOI
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10.12263/DZXB.20200952
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关键词
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多式联运
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个性化推荐
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网络表示学习
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粒子群算法
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特征工程
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地址
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山东理工大学计算机科学与技术学院, 山东, 淄博, 255049
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0372-2112 |
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学科
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自动化技术、计算机技术 |
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基金
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山东省高等学校优秀青年创新团队支持计划
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山东省自然科学基金
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文献收藏号
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CSCD:6982172
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