基于日志挖掘的个性化文献推荐算法

doi:10.3969/j.issn.1006-2475.2025.12.005

摘要/Abstract

摘要：
摘要：近年来，个性化推荐技术的探索在推荐系统研究领域中日益占据核心地位，其中，文献推荐作为这一领域的一个重要分支，正受到越来越多的关注与深入研究。针对目前科技文献发现系统中文献推荐算法未考虑用户行为偏好，及因用户数据稀疏导致大量用户无法获得文献推荐的问题，本文提出一种基于日志挖掘的个性化文献推荐算法。首先将用户行为记录日志初始化为用户-文献评分矩阵；然后，在深入分析用户兴趣偏好的过程中，综合考虑全局项目特征与地方评分信息，利用海明贴近度原理设计一种用户兴趣偏好相似度算法；最后，将该算法与加权JMSD相似度算法相融合，以此扩大推荐算法覆盖范围，进而大大提高推荐结果精度。实验采用NSTL文献服务系统实际用户行为日志数据，实验结果表明，该算法的总体表现优于其他基线算法，在Precision、Recall和F1这3个指标上平均提升0.8%、3.2%和1.9%，验证了本文推荐算法的有效性。该研究成果可应用到文献发现平台，进一步强化文献信息资源服务的智能化水平。

关键词: 关键词：日志挖掘, 用户偏好, 稀疏性, 个性化推荐, 全局特征, 推荐系统

Abstract: Abstract: In recent years, the exploration of personalized recommendation technology has increasingly occupied the core position in the field of recommendation system research. Among them, literature recommendation, as an important branch of this field, is receiving more and more attention and in-depth research. For the problems of that the current scientific and the literature recommendation algorithm in technological literature discovery system do not take into account the user’s behavior preferences and a large number of users cannot be recommended with literature due to sparse user data, this study proposes a personalized literature recommendation algorithm based on log mining. First of all, this study initializes the user behavior record log into a user-literature scoring matrix, and then in the process of in-depth analysis of user interest preferences, comprehensively considers the characteristics of global projects and local scoring information, and designs a user interest preference algorithm with the principle of Haiming’s closeness. Finally, the algorithm is fused with the weighted JMSD similarity algorithm to expand the coverage of the recommendation algorithm, and then greatly improve the accuracy of the recommendation results. The actual user behavior log data of NSTL literature service system was used in the experiment. The experimental results show that the algorithm’s overall performance is better than other baseline algorithms, with an average improvement of 0.8%, 3.2% and 1.9% in Precision, Recall and F1, which verified the effectiveness of the proposed algorithm. The research results can be applied to the literature discovery platform to further strengthen the intelligent level of document information resource service.

Key words: Key words: log mining, user preferences, sparseness, personalized recommendation, global feature, recommender systems

中图分类号:

中图分类号:TP391

张亚, 白海燕, 孟旭阳 . 基于日志挖掘的个性化文献推荐算法[J]. 计算机与现代化, 2025, 0(12): 32-37.

ZHANG Ya, BAI Haiyan, MENG Xuyang. Personalized Literature Recommendation Algorithm Based on Log Mining[J]. Computer and Modernization, 2025, 0(12): 32-37.

参考文献

[1] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C] // Proceedings of the 10th International Conference on World Wide Web. ACM, 2001:285-295
[2] 张宇,吴静. 基于LDA主题模型的协同过滤推荐算法[J]. 智能计算机与应用, 2024,14(2):190-194.
[3] AHMADIAN S，AFSHARCHI M，MEGHDADI M. A novel approach based on multi-view reliability measures to alleviate data sparsity in recommender systems[J]. Multimedia Tools and Applications, 2019,78(13):17763-17798.
[4] 李灵慧,王逊,王云沼,等. 融合动态K近邻Slope_One的协同过滤推荐算法[J]. 计算机与数字工程, 2024,52(1):156-161.
[5] 欧朝荣,胡军. 融合显隐式反馈的协同过滤推荐模型[J]. 控制与决策, 2024,39(3):1048-1056.
[6] LIN Z H, TIAN C X, HOU Y P, et a1．Improving graph collaborative filtering with neighborhood-enriched contrastive learning[C]//Proceedings of the ACM Web Conference 2022. ACM, 2022:2320-2329.
[7] TORRES N, MENDOZA M. Clustering approaches for top-k recommender systems[J]. International Journal on Artificial Intelligence Tools, 2019,28(5). DOI: 10.1142/S0218
21301950019.
[8] 史加荣,何攀. 结合矩阵补全的宽度协同过滤推荐算法[J]. 智能系统学报, 2024,19(2):299-306.
[9] HONG Y. A framework of three-way cluster analysis[C]// Proceedings of the International Joint Conference on Rough Sets. Springer, 2017:300-312.
[10] KOOHI H, KIANI K. User based collaborative filtering using fuzzy C-means[J]. Measurement, 2016,91:134-139.
[11] 赵宇,刘凤,舒巧媛,等. 基于马尔可夫聚类和混合协同过滤的电视节目推荐[J]. 计算机应用与软件, 2020,37(2):218-225.
[12] LIU Y, CHEN L, HE X N, et a1．Modelling high-order social relations for item recommendation[J]．IEEE Transactions on Knowledge and Data Engineering, 2022,34(9):4385-4397.
[13] RITA R, NUR U M, KRIDANTO S. Personalized neural network-based aggregation function in multi-criteria collaborative filtering[J]. Journal of King Saud University- Computer and Information Sciences, 2024,36(1). DOI: 10.1016/j.jksuci.2024.101922.
[14] GOEL C, SINHA B B. Cross-domain collaborative filtering: A deep neural network approach for accurate and diverse recommendations[J]. Procedia Computer Science, 2024,235:3408-3417.
[15] 王井. 一种基于订阅记录的图书协同过滤推荐方法研究[J]. 情报科学, 2020,38(3):54-59.
[16] ZHU X, SUN Y Q. Differential privacy for collaborative filtering recommender algorithm[C]// Proceedings of the 2016 ACM on International Workshop on Security and Privacy Analytics. ACM, 2016:9-16.
[17] KUZELEWSKA U. Multi-clustering used as neighborhood identification strategy in recommender systems[C]// International Conference on Dependability and Complex Systems. Springer, 2019:293-302.
[18] 罗园,陈希,周荣. 基于用户兴趣变化和社会化标注信息的协伺过滤推荐方法[J]. 系统工程, 2020,38(4):151-158.
[19] 苏庆,章静芳,林正鑫,等. 改进模糊划分聚类的协同过滤推荐算法[J]. 计算机工程与应用, 2019,55(5):118-123.
[20] LIU Y R, HUANG F Y, XIE X L, et al. Research on singular value decomposition recommendation algorithm based on data filling[J]. International Journal of Information Technologies and Systems Approach, 2023,16(3):1-15.
[21] 王运,倪静,马刚. 基于FunkSVD矩阵分解和相似度矩阵的推荐算法[J]. 计算机应用与软件, 2019,36(12):245-250.
[22] 董云薪,林耿,张清伟,等. 基于Apriori算法填充数据及改进相似度的推荐算法[J]. 计算机科学, 2022,49(11A). DOI: 10.11896/jsjkx.211000005.
[23] 王留芳,刘镇镇,魏蓝,等. 基于双因子混合加权相似度的协同过滤推荐算法[J]. 河南理工大学学报（自然科学版）, 2020,39(6):133-138.
[24] 李浩,梁京章,潘莹. 一种改进的兴趣相似度个性化推荐算法[J]. 计算机技术与发展, 2022,32(12):1-6.
[25] 邢长征,金媛. 填补法和改进相似度相结合的协同过滤算法[J]. 计算机应用研究, 2019,36(6):1643-1645.
[26] 吴锦昆,单剑锋. 基于改进型相似度的协同过滤算法的研究[J]. 计算机技术与发展, 2022,32(4):39-43.
[27] BOBADILLA J, SERRADILLA F, BERNAL J. A new collaborative filtering metric that improves the behavior of recommender systems[J]. Knowledge-Based Systems, 2010,23(6):520-528.