Library and Information Service

Library and Information Service >

2023 , Vol. 67 >Issue 3: 72 - 84

DOI: https://doi.org/10.13266/j.issn.0252-3116.2023.03.007

INFORMATION RESEARCH

Subject Term Recommendation Based on the Fusion of Explicit & Implicit Information and One-class Collaborative Filtering

  • Li Shuqing ,
  • Huang Jinwang ,
  • Ma Dandan ,
  • Zhang Zhiwang
Expand
  • School of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023

Received date: 2022-08-17

  Revised date: 2022-11-26

  Online published: 2023-02-24

Fold

Abstract

[Purpose/Significance] The proposed one-class collaborative filtering algorithm with the fusion of explicit and implicit information has a remarkable effect in the field of literature subject term recommendation, and improves the precision of subject term recommendation for scholar and literature. [Method/Process] By constructing a matrix decomposition model based on literature richness and subject term popularity, the correlation probability of literature and subject terms that do not appear in the current literature was measured, and these subject terms could be divided into implicit related subject terms and implicit unrelated subject terms of literature according to the correlation probability. For these two kinds of subject terms, two different weight prediction methods of subject terms were proposed, namely, AutoRec Filling with Preference Coefficient and Zero Filling. [Result/Conclusion] The experiment on SD4AI, a scientific and technological literature dataset oriented to the field of artificial intelligence, shows that compared with various typical collaborative filtering methods, MAE and FCP have respectively improved the recommendation effect of predicting the weight of subject terms and identifying high weight subject terms, with the maximum increase of 16.07% and 16.83%, while the maximum value of P@N and NDCG@N is 22.37% and 27.06% respectively.

Key words： subject term recommendation; subject term expansion; one-class collaborative filtering; term relevance; term weight

Cite this article

Li Shuqing , Huang Jinwang , Ma Dandan , Zhang Zhiwang . Subject Term Recommendation Based on the Fusion of Explicit & Implicit Information and One-class Collaborative Filtering[J]. Library and Information Service, 2023 , 67(3) : 72 -84 . DOI: 10.13266/j.issn.0252-3116.2023.03.007

References

[1] 李辉, 曾文, 谭晓, 等.科技大数据资源平台建设研究[J].科技情报研究, 2022, 4(1):71-77.
[2] 《中国科技期刊发展蓝皮书(2021)》编写组.《中国科技期刊发展蓝皮书(2021)——开放科学环境下的学术出版专题》 内容简介[J].中国科技期刊研究, 2021, 32(12):1477-1480.
[3] GU X, BLACKMORE K L.Recent trends in academic journal growth[J].Scientometrics, 2016, 108(2):693-716
[4] JIN J, GENG Q, MOU H, et al.Author-Subject-Topic model for reviewer recommendation[J].Journal of information science, 2019, 45(4):554-570.
[5] WAHEED W, IMRAN M, RAZA B, et al.A hybrid approach towards research paper recommendation using centrality measures and author ranking[J].IEEE access, 2019, 7:33145-33158.
[6] HE M, PAN W, MING Z.BAR:Behavior-aware recommendation for sequential heterogeneous one-class collaborative filtering[J].Information sciences, 2022, 608(8):881-899.
[7] 李亚梅, 秦春秀, 马续补.基于科研人员情境化主题偏好的科技文献协同推荐研究[J].情报理论与实践, 2021, 44(12):180-189.
[8] YIN X, WANG H, YIN P, et al.A co-occurrence based approach of automatic keyword expansion using mass diffusion[J].Scientometrics, 2020, 124(3):1885-1905.
[9] 崔婉秋, 李昕, 孟祥福, 等.耦合关系分析下的Top-k关键字推荐方法[J].小型微型计算机系统, 2016, 37(8):1686-1691.
[10] 袁莎, 唐杰, 顾晓韬.开放互联网中的学者画像技术综述[J].计算机研究与发展, 2018, 55(9):1903-1919.
[11] LEE Y, WON H, SHIM J, et al.A hybrid collaborative filteringbased product recommender system using search keywords[J].Journal of intelligence and information systems, 2020, 26(1):151-166.
[12] 李伟卿, 池毛毛, 王伟军.面向用户长短期偏好调节的可解释个性化推荐方法研究[J].图书情报工作, 2021, 65(12):101-111.
[13] 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.
[14] 谢豪, 吴雪华, 陈茜, 等.融合多维特征的学术文献下载行为预测研究[J].图书情报工作, 2021, 65(12):112-121.
[15] 李媛媛, 李旭晖.结合本体与社会化标签的用户动态兴趣建模研究[J].情报学报, 2020, 39(4):436-449.
[16] ZANGERLE E, GASSLER W, SPECHT G.Using tag recommendations to homogenize folksonomies in microblogging environments[C]//Proceedings of international conference on social informatics.Berlin:Springer, 2011:113-126.
[17] HONG L, DAVISON B D.Empirical study of topic modeling in Twitter[C]//Proceedings of the first workshop on social media analytics.New York:ACM, 2010:80-88.
[18] 谭晓, 李辉, 许海云.基于多维数据知识内容和关联深层融合的知识发现研究综述[J].科技情报研究, 2021, 3(4):58-68.
[19] YUAN W, QU J, JIE L, et al.What to tag your microblog:hashtag recommendation based on topic analysis and collaborative filtering[C]//Proceedings of Asia-Pacific Web conference.Cham:Springer, 2014:610-618.
[20] 毕强, 刘健.基于领域本体的数字文献资源聚合及服务推荐方法研究[J].情报学报, 2017, 36(5):452-460.
[21] 尹志强.融合评分矩阵与评论文本的混合推荐算法的研究[D].北京:北京交通大学, 2021.
[22] 徐俊, 张政, 杜宣萱, 等.基于项目语义的协同过滤冷启动推荐算法研究[J].小型微型计算机系统, 2021, 42(11):2246-2251.
[23] NAJAFABADI M K, MAHRIN M N R.A systematic literature review on the state of research and practice of collaborative filtering technique and implicit feedback[J].Artificial intelligence review, 2016, 45(2):167-201.
[24] HAN H, HUANG M, ZHANG Y, et al.An extended-tag-induced matrix factorization technique for recommender systems[J].Information, 2018, 9(6):143.
[25] MORADI M, HAMIDZADEH J.Ensemble-based top-k recommender system considering incomplete data[J].Journal of AI and data mining, 2019, 7(3):393-402.
[26] CHEN J, DONG H, WANG X, et al.Bias and debias in recommender system:a survey and future directions[EB/OL].[2022-07-15].https://arxiv.org/abs/2010.03240.
[27] LEE D, KANG S, JU H, et al.Bootstrapping user and item representations for one-class collaborative filtering[C]//Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval.New York:ACM, 2021:317-326.
[28] HU Y, KOREN Y, VOLINSKY C.Collaborative filtering for implicit feedback datasets[C]//Proceedings of the eighth IEEE international conference on data mining.Italy:IEEE, 2008:263-272.
[29] PAN R, ZHOU Y, CAO B, et al.One-class collaborative filtering[C]//Proceedings of the eighth IEEE international conference on data mining.Italy:IEEE, 2008:502-511.
[30] HE X, ZHANG H, KAN M Y, et al.Fast matrix factorization for online recommendation with implicit feedback[C]//Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval.New York:ACM, 2016:549-558.
[31] CHENG X, FENG L, GUI Q.Collaborative filtering algorithm based on data mixing and filtering[J].International journal of performability engineering, 2019, 15(8):2267-2276.
[32] CREMONESI P, KOREN Y, TURRIN R.Performance of recommender algorithms on top-n recommendation tasks[C]//Proceedings of the fourth ACM conference on recommender systems.New York:ACM, 2010:39-46.
[33] CHAE D K, KANG J S, KIM S W, et al.Rating augmentation with generative adversarial networks towards accurate collaborative filtering[C]//Proceedings of World Wide Web conference.New York:ACM, 2019:2616-2622.
[34] ORTEGA F, BOBADILLA J, GUTIÉRREZ A, et al.Artificial intelligence scientific documentation dataset for recommender systems[J].IEEE access, 2018, 6:48543-48555.
[35] 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.New York:ACM, 2001:285-295.
[36] XIA W, HE L, GU J, et al.Effective collaborative filtering approaches based on missing data imputation[C]//Proceedings of the fifth international joint conference on INC, IMS and IDC.Seoul:IEEE, 2009:534-537.
[37] LEMIRE D, MACLACHLAN A.Slope one predictors for online rating-based collaborative filtering[C]//Proceedings of international conference on data mining, Society for Industrial and Applied Mathematics.Philadelphia:SIAM, 2005:471-475.
[38] SEDHAIN S, MENON A K, SANNER S, et al.Autorec:Autoencoders meet collaborative filtering[C]//Proceedings of the 24th international conference on World Wide Web.New York:ACM, 2015:111-112.
[39] KOREN Y.Factorization meets the neighborhood:a multifaceted collaborative filtering model[C]//Proceedings of the 14th international conference on knowledge discovery and data mining.New York:ACM, 2008:426-434.
[40] HE X, LIAO L, ZHANG H, et al.Neural collaborative filtering[C]//Proceedings of the 26th international conference on World Wide Web.New York:ACM, 2017:173-182.
[41] LEE J, HWANG W S, PARC J, et al.$ l $-Injection:toward effective collaborative filtering using uninteresting items[J].IEEE transactions on knowledge and data engineering, 2017, 31(1):3-16.
[42] WU Y, DUBOIS C, ZHENG A X, et al.Collaborative denoising auto-encoders for top-n recommender systems[C]//Proceedings of the ninth ACM international conference on Web search and data mining.New York:ACM, 2016:153-162.
[43] HE X, DENG K, WANG X, et al.Lightgcn:Simplifying and powering graph convolution network for recommendation[C]//Proceedings of the 43rd International ACM SIGIR conference on research and development in information retrieval.New York:ACM, 2020:639-648.
Options
Outlines

/

〈 〉

    Copyrights © LIS Press Co., Ltd. Reproduction is prohibited without permission.