|
|
|
| Online Publication Recommendation Based on Weighted Features of User Multiple Interest Drift |
Qian Cong1,2,Qi Jianglei1,Ding Hao1( ) |
1School of Information Management, Nanjing University, Nanjing 210023, China
2Department of Chinese Language and Literature, Hetao University, Bayannur 015000, China |
|
|
|
|
Abstract [Objective] This paper improves the Reinforced Latent Factor Model with user multi-adaptive preference feature temporal weighting, aiming to improve the accuracy of recommendations. [Methods] Building upon the Temporal Potential Factor Model, we further integrated user preferences from different periods, such as interest forgetting features, publication interest overlap, and semantic similarity of comments. The user rating matrix is weighted and decomposed based on preference weights to capture the multiple preference changes of users towards different publications at different times. [Results] We conducted comparison experiments with four baseline methods based on temporal matrix factorization with three datasets. The proposed model’s precision was 9.26% higher than TDMF, 17.35% higher than TMRevCo, 38.63% higher than BPTF, and 26.24% higher than TCMF. This demonstrates that the proposed model is more accurate in extracting user temporal features. [Limitations] The analysis of interest drift evolution depends on historical user data. When the data is too sparse, alternative user information is required for a cold start. [Conclusions] The proposed model considers user forgetting and comment evolution features, effectively capturing user temporal interest drift and reflecting the evolving relationship of users’ interest in publications. It improves the accuracy of recommendations.
|
|
Received: 01 August 2022
Published: 08 October 2023
|
|
|
| Fund:National Social Science Fund of China(20&ZD154);Key Project of the Open Project of the Yellow River Several Character Bend Cultural Innovation Research Base(JZW2022001);Postgraduate Research & Practice Innovation Program of Jiangsu Province(KYCX22_0074) |
|
Corresponding Authors:
Ding Hao，ORCID：0000-0003-3528-5686， E-mail： dinghao@smail.nju.edu.cn。
|
| [1] |
马鸿健, 张耘凡, 陈金, 等. 高校图书推荐系统设计与实现[J]. 信息技术与信息化, 2021(12):49-52.
|
| [1] |
(Ma Hongjian, Zhang Yunfan, Chen Jin, et al. Design and Implementation of University Book Recommendation System[J] Information Technology and Informatization, 2021 (12): 49-52.)
|
| [2] |
温慧仪. 新媒体环境下公共图书馆与移动阅读平台合作实践研究——以QQ阅读为例[J]. 河北科技图苑, 2021, 34(1):61-65.
|
| [2] |
(Wen Huiyi. Research on the Cooperation Between Public Library and Mobile Reading Platform in the New Media Environment -- Taking QQ Reading as an Example[J]. Hebei Library Journal of Science and Technology, 2021, 34 (1): 61-65.)
|
| [3] |
陈俊鹏, 虞为. 基于快数据处理技术的数字阅读推荐模式研究[J]. 图书馆工作与研究, 2015(12):27-30.
|
| [3] |
(Chen Junpeng, Yu Wei. Research on Digital Reading Recommendation Mode Based on Fast Data Processing Technology[J]. Library Work and Study, 2015(12): 27-30.)
|
| [4] |
刘一鸣, 王佳佳. 基于区块链技术的图书馆数字阅读精准推荐研究[J]. 图书馆理论与实践, 2022(2):107-115.
|
| [4] |
Liu Yiming, Wang Jiajia. Research on Accurate Digital Reading Recommendation of Library Based on(Blockchain Technology[J]. Library Theory and Practice, 2022(2): 107-115.)
|
| [5] |
宋欢迎, 贾雪雯. 我国数字阅读研究进展、主题及特点[J]. 中国编辑, 2022(1):78-84.
|
| [5] |
(Song Huanying, Jia Xuewen. Research Progress, Theme and Characteristics of Digital Reading in China[J]. Chinese Editors Journal, 2022(1): 78-84.)
|
| [6] |
熊力. 基于社交网络的网络出版物推荐机制[D]. 武汉: 武汉科技大学, 2014.
|
| [6] |
(Xiong Li. Online Publications Recommendation Mechanism Based on Social Platforms[D]. Wuhan: Wuhan University of Science and Technology, 2014.)
|
| [7] |
肖倩, 韩婷, 张聪. 社会化媒体环境中的数字阅读物推荐及其用户体验研究——以豆瓣阅读为例[J]. 科技与出版, 2014(11):65-69.
|
| [7] |
(Xiao Qian, Han Ting, Zhang Cong. Research on Digital Reading Recommendation and User Experience in Social Media Environment -- Taking Douban Reading as an Example[J]. Science-Technology & Publication, 2014 (11): 65-69.)
|
| [8] |
高晓晶, 侯壮, 雷萍, 等. 新媒体环境下校园网络阅读的研究与实践——以“书香成电”为例[J]. 图书馆杂志, 2016, 35(4):52-59.
|
| [8] |
(Gao Xiaojing, Hou Zhuang, Lei Ping, et al. Research and Practice on Network Reading on Campus in the New Media Environment: Take the “Shuxiang UESTC” for Example[J]. Library Journal, 2016, 35(4): 52-59.)
|
| [9] |
李树青, 苗蕾, 严梓豪, 等. 学术虚拟社区知识交流效率研究方法综述[J]. 科技情报研究, 2022, 4(1):1-12.
|
| [9] |
(Li Shuqing, Miao Lei, Yan Zihao, et al. A Survey of Research Methods on Knowledge Exchange Efficiency of Academic Virtual Community[J]. Scientific Information Research, 2022, 4(1): 1-12.)
|
| [10] |
梁少博, 李金玲. 移动数字阅读平台用户转移行为研究[J]. 图书情报工作, 2022, 66(2):99-108.
doi: 10.13266/j.issn.0252-3116.2022.02.011
|
| [10] |
(Liang Shaobo, Li Jinling. Research on User’s Switching Behaviors of Mobile Digital Reading Platforms[J]. Library and Information Service, 2022, 66(2): 99-108.)
doi: 10.13266/j.issn.0252-3116.2022.02.011
|
| [11] |
郑德俊, 李杨, 沈军威, 等. 移动阅读服务平台的用户流失因素分析——以“微信读书”平台为例[J]. 情报理论与实践, 2019, 42(8):78-82.
|
| [11] |
(Zheng Dejun, Li Yang, Shen Junwei, et al. A Study on the Influence Factors of User Exodus in Mobile Reading Platform:Taking “WeChat Reading” as an Example[J]. Information Studies: Theory & Application, 2019, 42(8):78-82.)
|
| [12] |
董杨帆. 改进协同过滤算法的图书推荐系统设计与实现[J]. 情报探索, 2021(7):46-51.
|
| [12] |
(Dong Yangfan. Design and Implementation of Book Recommendation System with Improved Collaborative Filtering Algorithm[J]. Information Research, 2021(7):46-51.)
|
| [13] |
丁浩, 胡广伟, 王婷, 等. 基于时序漂移的潜在因子模型推荐方法[J]. 数据分析与知识发现, 2022, 6(10): 1-8.
|
| [13] |
(Ding Hao, Hu Guangwei, Wang Ting, et al. Recommendation Method for Potential Factor Model Based on Time Series Drift[J]. Data Analysis and Knowledge Discovery, 2022, 6(10): 1-8.)
|
| [14] |
Koren Y. Collaborative Filtering with Temporal Dynamics[J]. Communications of the ACM, 2010, 53(4):89-97.
|
| [15] |
Chua F C T, Oentaryo R J, Lim E P. Modeling Temporal Adoptions Using Dynamic Matrix Factorization[C]// Proceedings of the 13th International Conference on Data Mining. IEEE, 2013: 91-100.
|
| [16] |
Li K, Zhou X, Lin F, et al. Sparse Online Collaborative Filtering with Dynamic Regularization[J]. Information Sciences, 2019, 505: 535-548.
doi: 10.1016/j.ins.2019.07.093
|
| [17] |
Xiao Y, Wang G, Hsu C H, et al. A Time-sensitive Personalized Recommendation Method Based on Probabilistic Matrix Factorization Technique[J]. Soft Computing, 2018, 22(20): 6785-6796.
doi: 10.1007/s00500-018-3406-4
|
| [18] |
朱振国, 刘民康, 赵凯旋. 基于用户联合相似度的推荐算法[J]. 计算机系统应用, 2018, 27(5):126-132.
|
| [18] |
(Zhu Zhenguo, Liu Minkang, Zhao Kaixuan Recommendation Algorithm Based on Combined Similarity of Users[J] Computer Systems & Applications, 2018, 27 (5):126-132.)
|
| [19] |
Kong F, Li J, Lv Z. Construction of Intelligent Traffic Information Recommendation System Based on Long Short-Term Memory[J]. Journal of Computational Science, 2018, 26: 78-86.
doi: 10.1016/j.jocs.2018.03.010
|
| [20] |
Zhang W, Ding G, Chen L, et al. Generating Virtual Ratings from Chinese Reviews to Augment Online Recommendations[J]. ACM Transactions on Intelligent Systems & Technology, 2013, 4(1):1-17.
|
| [21] |
Zhao X, Niu Z, Chen W. Opinion-based Collaborative Filtering to Solve Popularity Bias in Recommender Systems[C]// Proceedings of International Conference on Database and Expert Systems Applications. Springer, 2013: 426-433.
|
| [22] |
王红, 陈功平. 隐含语义模型在推荐系统中的应用研究[J]. 广东技术师范大学学报(社会科学版), 2020(6):9-13.
|
| [22] |
(Wang Hong, Chen Gongping. A Study on the Application of the Implicit Semantic Model in Recommender Systems[J]. Journal of Guangdong Polytechnic Normal University, 2020(6): 9-13.)
|
| [23] |
He T, Chen Z, Liu J, et al. An Empirical Study on User-topic Rating Based Collaborative Filtering Methods[J]. World Wide Web, 2017, 20(4):815-829.
doi: 10.1007/s11280-016-0412-2
|
| [24] |
Abdelwahab A, Sekiya H, Matsuba I, et al. An Efficient Collaborative Filtering Algorithm Using SVD-free Latent Semantic Indexing and Particle Swarm Optimization[C]// Proceedings of International Conference on Natural Language Processing and Knowledge Engineering. IEEE, 2009.
|
| [25] |
Cheng Y, Qiu G, Bu J, et al. Model Bloggers’ Interests Based on Forgetting Mechanism[C]// Proceedings of the 17th International Conference on World Wide Web. 2008: 1129-1130.
|
| [26] |
Milovančević N S, Gračanac A. Time and Ontology for Resource Recommendation System[J]. Physica A: Statistical Mechanics and Its Applications, 2019, 525: 752-760.
doi: 10.1016/j.physa.2019.04.005
|
| [27] |
Li H, Wang W, Liu Z, et al. A Novel Locality-sensitive Hashing Relational Graph Matching Network for Semantic Textual Similarity Measurement[J]. Expert Systems with Applications, 2022, 207: 117832.
doi: 10.1016/j.eswa.2022.117832
|
| [28] |
Kuhn H W, Tucker A W. Nonlinear Programming[A]// Traces and Emergence of Nonlinear Programming[M]. 2014: 247-258.
|
| [29] |
Ni J, Li J, McAuley J. Justifying Recommendations Using Distantly-Labeled Reviews and Fine-Grained Aspects[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019: 188-197.
|
| [30] |
Rafailidis D, Kefalas P, Manolopoulos Y. Preference Dynamics with Multimodal User-Item Interactions in Social Media Recommendation[J]. Expert Systems with Applications, 2017, 74: 11-18.
doi: 10.1016/j.eswa.2017.01.005
|
| [31] |
Wu T, Feng Y, Sang J X, et al. A Novel Recommendation Algorithm Incorporating Temporal Dynamics, Reviews and Item Correlation[J]. IEICE Transactions on Information and Systems, 2018, 101(8): 2027-2034.
|
| [32] |
Xiong L, Chen X, Huang T K, et al. Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization[C]// Proceedings of the 2010 SIAM International Conference on Data Mining. 2010: 211-222.
|
| [33] |
Zhang C, Wang K, Yu H, et al. Latent Factor Transition for Dynamic Collaborative Filtering[C]// Proceedings of the 2014 SIAM International Conference on Data Mining. SIAM, 2014:452-460.
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
