Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (7): 2372-2377.doi: 10.13229/j.cnki.jdxbgxb.20240603

Previous Articles    

Query algorithm for massive Web multi-attribute data that integrates user interests

Jian-ping SUN1(),Zhi-he LI2   

  1. 1.School of Artificial Intelligence，Beijing Normal University，Beijing 100088，China
    2.Research Institute of Information Technology and Intelligence，Shanxi Normal University，Taiyuan 030031，China
  • Received:2024-05-31 Online:2025-07-01 Published:2025-09-12

RICH HTML

 0   

PDF (PC)

34

Abstract:

In order to improve customer satisfaction， massive Web multi-attribute data queries are conducted by integrating user interests. Based on the attributes of the data， obtain the topic vector of Web multi-attribute data， and consider the forgetting factor to dynamically manage user interests， in order to more accurately reflect the current interests of users. On this basis， cosine similarity is used to calculate the similarity between Web multi-attribute data feature vectors and user interest topic vectors， and Borda counting method is used to integrate the initialization query list and personalized query list to obtain the final query list that comprehensively considers user interests， achieving a personalized data query experience. The experimental results show that the algorithm can achieve Web multi-attribute data queries and has the ability to handle ambiguous queries. And as the number of queries increases， user satisfaction approaches 100%. This article demonstrates that the algorithm can further understand the needs of users based on their query content， and provide users with more accurate query results.

Key words: Web multi-attribute data, reverse user frequency index, forgetting factor, dynamic interest model, expansion mechanism, similarity

CLC Number: 

  • TP391

Fig.1

Query mechanism"

Table 1

Web property index storage form table"

表名RowKey索引（列族）

SOURCE

YYYY

MM-DD索引：attrl 索引：attr2 索引：attrn
排序Urls 排序Urls 排序Urls

Table 2

Setting of experimental parameters"

参数名称参数值
初始化查询列表长度10
个性化查询列表长度5
遗忘因子0.7
数据属性权重［0.2， 0.3， 0.1， 0.2， 0.2］
交互强度（点击）1
交互强度（长时间浏览）2

Fig.2

Analysis of query results"

Table 3

Comparison of data query performance under different algorithms"

查询类型本文算法文献［7］算法文献［10］算法
RS相比本文算法降低/%RS相比本文算法降低/%
所有查询0.6980.5670.1880.3520.496
非最优查询0.4930.4260.1360.3120.367
最优查询0.9830.8250.1610.5690.421

Table 4

Performance comparison of three algorithms in handling ambiguous queries"

点击熵本文算法文献［7］算法文献［10］算法
RS相比本文算法降低/%RS相比本文算法降低/%
低于1.70.7280.60516.90.53526.6
高于等于1.70.5030.4686.80.41218.1

Fig.3

Comparison of user satisfaction with Webmulti-attribute data query results"

[1] 赵红梅, 肖明, 白宇, 等. 面向用户偏好的动态网页数据交互式查询算法[J]. 吉林大学学报: 理学版, 2024, 62(2): 417-422.
Zhao Hong-mei, Xiao Ming, Bai Yu, et al. Interactive query algorithm for dynamic web page data based on user preference[J]. Journal of Jilin University(Science Edition),2024,62(2):417-422.
[2] 孙琛琛, 申德荣, 肖迎元, 等. 面向查询式实体解析的多属性数据索引技术[J]. 软件学报, 2022, 33(6): 2331-2347.
Sun Chen-chen, Shen De-rong, Xiao Ying-yuan, et al. Multi-attribute data indexing for query based entity resolution[J]. Journal of Software,2022,33(6):2331-2347.
[3] 王子泓, 邵蓥侠, 何吉元, 等. 基于多空间属性信息融合的序列推荐[J]. 计算机科学, 2024, 51(3):102-108.
Wang Zi-hong, Shao Ying-xia, He Ji-yuan, et al. Sequential recommendation based on multi-space attribute information fusion[J]. Computer Science,2024,51(3):102-108.
[4] 翁彬月, 秦永彬, 黄瑞章, 等. NEMTF: 基于多维度文本特征的新闻网页信息提取方法[J]. 计算机应用研究, 2022, 39(4): 1043-1048.
Weng Bin-yue, Qin Yong-bin, Huang Rui-zhang,et al. NEMTF:method of news Web content extraction based on multi-dimensional text features[J]. Application Research of Computers,2022,39(4):1043-1048.
[5] 蒲岍岍, 雷航, 李贞昊, 等.增强列表信息和用户兴趣的个性化新闻推荐算法[J]. 计算机科学, 2022, 49(6): 142-148.
Pu Yan-yan, Lei Hang, Li Zhen-hao,et al. Personalized news recommendation algorithm with enhanced list[J]. Computer Science, 2022, 49(6):1 42-148.
[6] 聂卉, 邱以菲. 融合用户兴趣及评论效用的评论信息推荐[J]. 图书情报工作, 2021, 65(10): 68-78.
Nie Hui, Qiu Yi-fei. Integrating user interests with review helpfulness for review recommendation[J]. Library and Information Service,2021, 65(10): 68-78.
[7] 杨矫云, 郭思伊, 李廉. 基于Pac算法的流数据top-k实时查询[J].华中科技大学学报:自然科学版,2021, 49(2):56-61.
Yang Jiao-yun, Guo Si-yi, Li Lian. Pac based top-k real-time query algorithm for streaming data [J]. Journal of Huazhong University of Science and Technology(Nature Science Edition),2021, 49(2):56-61.
[8] 高俊杰, 杨帆. 基于群体智能的半结构化数据查询优化算法[J]. 计算机仿真, 2021, 38(8): 381-385.
Gao Jun-jie, Yang Fan. Semi-structured data query optimization algorithm based on swarm intelligence [J]. Computer Simulation,2021, 38(8): 381-385.
[9] 罗芳, 李春花, 周可, 等.基于多属性的海量Web数据关联存储及检索系统[J]. 计算机工程与科学, 2022, 36(3): 404-410.
Luo Fang, Li Chun-hua, Zhou Ke, et al. An associated storage and retrieval system of massive Web data based on multi-attributes[J]. Computer Engineering and Science,2022, 36(3): 404-410.
[10] Tung V, Salvatore A, Giulio J,et al. Spoken conversational context improves query auto-completion in web search[J]. ACM Transactions on Information Systems, 2021, 39(3): 1-32.
[11] 张星, 张兴. dckpdp:改进k-prototype聚类的差分隐私混合属性数据发布方法[J]. 计算机应用研究, 2022, 39(1): 249-253.
Zhang Xing, Zhang Xing. Differential privacy mixed attribute data publishing method for improved k-prototype clustering[J]. Application Research of Computers, 2022, 39(1): 249-253.
[12] 虞文波, 游进国, 牛祥虞. 基于强化学习的数据库多属性索引推荐[J]. 计算机应用研究, 2023, 40(6): 1789-1793.
Yu Wen-bo, You Jin-guo, Niu Xiang-yu. Mira:database multi-attribute index recommendation based on reinforcement learning[J]. Application Research of Computers, 2023, 40(6): 1789-1793.
[13] 麻天, 余本国, 张静, 等. 基于混合聚类与融合用户兴趣的协同过滤推荐算法[J]. 电子技术应用, 2022, 48(4): 29-33.
Ma Tian, Yu Ben-guo, Zhang Jing, et al. Collaborative filtering recommendation algorithm based on hybrid clustering and user preferences fusion[J]. Application of Electronic Technique,2022, 48(4): 29-33.
[14] 凌宇, 单志龙. 基于兴趣增强的知识概念推荐系统[J].计算机应用, 2023, 43(12): 3697-3702.
Ling Yu, Shan Zhi-long. Knowledge concept recommendation system based on interest enhancement[J]. Journal of Computer Applications, 2023, 43(12): 3697-3702.
[15] 张彬, 徐建民, 吴姣. 大数据环境下基于知识图谱的用户兴趣扩展模型研究[J]. 现代情报, 2021, 41(8): 36-44.
Zhang Bin, Xu Jian-min, Wu Jiao. Research on user interest expansion model based on knowledge graph in big data environment[J]. Modern Information, 2021, 41(8): 36-44.
[1] Xiang-jiu CHE,Liang LI. Graph similarity measurement algorithm combining global and local fine-grained features [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(7): 2365-2371.
[2] Lan-fang ZHANG,Gen-ze LI,Ting-yu LIU,Bo YU. Mechanism and modeling of car⁃following behavior under local multi⁃vehicle influence [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(3): 963-973.
[3] Feng LYU,Nian LI,Zhuang-zhuang FENG,Yang-hang ZHANG. Method of collaborative filtering recommendation of personalized product-service system based on user [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(7): 1935-1942.
[4] Chao-jian FANG,Xin-rong HU. Privacy-sensitive data filtering algorithm based on fuzzy approximation [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(4): 1174-1180.
[5] Fu-heng QU,Tian-yu DING,Yang LU,Yong YANG,Ya-ting HU. Fast image codeword search algorithm based on neighborhood similarity [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1865-1871.
[6] Ling ZHAI,Xu CUI. Text information similarity search algorithm based on segment estimation and PageRank [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 910-915.
[7] Su-ming KANG,Ye-e ZHANG. Hadoop⁃based local timing link prediction algorithm across social networks [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 626-632.
[8] Gui-xiang SHEN,Jun ZHENG,Ying-zhi ZHANG,Jie SONG,Zhe-wen LI. Risk analysis of machining center failure mode based on multi⁃attribute group decision making [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 338-344.
[9] Hai-long WANG,Lin LIU,Min LIN,Dong-mei PEI. Music personalized recommendation algorithm based on k⁃means clustering algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1845-1850.
[10] Hong-wei ZHAO,Peng WANG,Li-li FAN,Huang-shui HU,Ping-ping LIU. Similarity retention instance retrieval method [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 2045-2050.
[11] GUI Chun, HUANG Wang-xing. Network clustering method based on improved label propagation algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1600-1605.
[12] WANG Xu, OUYANG Ji-hong, CHEN Gui-fen. Measurement of graph similarity based on vertical dimension sequence dynamic time warping method [J]. 吉林大学学报(工学版), 2018, 48(4): 1199-1205.
[13] TAN Si-qiao, ZHANG Xi, LI Qian, AI Chen. Information push model-building based on maximum mutual information coefficient [J]. 吉林大学学报(工学版), 2018, 48(2): 558-563.
[14] WANG Xu, OUYANG Ji-hong, CHEN Gui-fen. Heuristic algorithm of all common subsequences of multiple sequences for measuring multiple graphs similarity [J]. 吉林大学学报(工学版), 2018, 48(2): 526-532.
[15] LUAN Wen-peng, LIU Yong-lei, WANG Peng, JIN Zhi-gang, WANG Jian. Novel universal security mechanism for energy internet based on trusted platform module [J]. 吉林大学学报(工学版), 2017, 47(6): 1933-1938.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd