摘要
【目的】为缓解历史数据稀疏以及类别偏好与项目时效因素对推荐算法性能的影响,提高推荐精度。【方法】采用哈夫曼编码融合类别偏好和项目时效因素的评分数据;求解用户、项目评分相似矩阵,并由DeepWalk模型挖掘其潜在特征向量;融合用户、项目特征向量,并由极限学习机预测项目评分。【结果】在MovieLens和Yahoo!R3数据集上,随着训练集比例的增加,预测精度最高分别达95.52%和98.01%,运行时间仅分别为19.93 s和22.21 s,较性能次优的XGB-CF算法的预测精度分别提高0.84和2.10个百分点,运行时间分别缩短7.92 s和9.79 s。【局限】算法未考虑用户评论的文本信息及多元化的项目类别。【结论】所提算法较对比算法具有更高的预测精度,可用于个性化推荐。
[Objective]This paper addresses the influence of historical data sparsity,category preference,and item timeliness on the performance of recommendation algorithms and improves their accuracy.[Methods]Firstly,we used Huffman Coding to encode the rating data with category preference and item popularity.Then,we computed the score similarity matrices of users and projects.We also extracted their latent feature vectors using the DeepWalk model.Finally,we fused the user and project feature vectors and predicted the project ratings with Extreme Learning Machines.[Results]We examined the new model on the MovieLens and Yahoo!R3 datasets.As the proportion of the training set increased,the highest prediction accuracies reached 95.52%and 98.01%,respectively,with a runtime of only 19.93s and 22.21s.The proposed algorithm outperformed the XGB-CF algorithm in terms of prediction by 0.84 and 2.10 percentage points,respectively,with a runtime reduction of 7.92s and 9.79s.[Limitations]The proposed algorithm did not consider the textual information from user comments and diversified project categories.[Conclusions]Our new algorithm demonstrates higher prediction accuracy than the reference algorithm and can be used for personalized recommendations.
作者
杨怀珍
张静
李雷
Yang Huaizhen;Zhang Jing;Li Lei(Business School,Guilin University of Electronic Technology,Guilin 541004,China;Business School,Guilin University of Technology,Guilin 541004,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2023年第7期136-145,共10页
Data Analysis and Knowledge Discovery
基金
国家自然科学基金项目(项目编号:72074058,71562008)
广西研究生教育创新计划项目(项目编号:YCBZ2022112)的研究成果之一。
关键词
混合推荐
项目时效因素
极限学习机
类别偏好
Hybrid Recommendation
Project Timeliness Factor
Extreme Learning Machine
Category Preference
