Academic journal contribution recommendation algorithm based on author preferences

doi:10.11772/j.issn.1001-9081.2021010185

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (1): 50-56.DOI: 10.11772/j.issn.1001-9081.2021010185

Special Issue: 人工智能

• Artificial intelligence • Previous Articles Next Articles

Academic journal contribution recommendation algorithm based on author preferences

Yongfeng DONG¹^,²^,³, Xiangqian QU¹^,²^,³, Linhao LI¹^,²^,³(), Yao DONG¹^,²^,³

^1.School of Artificial Intelligence，Hebei University of Technology，Tianjin 300401，China
^2.Hebei Province Key Laboratory of Big Data Calculation （Hebei University of Technology），Tianjin 300401，China
^3.Hebei Data Driven Industrial Intelligent Engineering Research Center （Hebei University of Technology），Tianjin 300401，China

Received:2021-02-03 Revised:2021-03-27 Accepted:2021-04-14 Online:2021-04-29 Published:2022-01-10
Contact: Linhao LI
About author:DONG Yongfeng， born in 1977， Ph. D.， professor. His research interests include artificial intelligence， knowledge graph.
QU Xiangqian， born in 1995， M. S. candidate. His research interests include recommended system， big data and intelligent computing.
LI Linhao， born in 1989， Ph. D.， lecturer. His research interests include intelligent monitoring， knowledge tracking.
DONG Yao， born in 1982， Ph. D. candidate， senior experimentalist. Her research interests include knowledge graph， data mining.
Supported by:
National Natural Science Foundation of China(61902106);Natural Science Foundation of Tianjin(19JCZDJC40000);Beidou Technology Transformation and Industrialization Foundation of Beihang(BARI2001);Science and Technology Research Project of Hebei Province Colleges and Universities(QN2021213)

基于作者偏好的学术投稿刊物推荐算法

董永峰¹^,²^,³, 屈向前¹^,²^,³, 李林昊¹^,²^,³(), 董瑶¹^,²^,³

^1.河北工业大学人工智能与数据科学学院, 天津 300401
^2.河北省大数据计算重点实验室(河北工业大学), 天津 300401
^3.河北省数据驱动工业智能工程研究中心(河北工业大学), 天津 300401

通讯作者: 李林昊
作者简介:董永峰（1977—），男，河北定州人，教授，博士，CCF会员，主要研究方向：人工智能、知识图谱
屈向前（1995—），男，河北保定人，硕士研究生，主要研究方向：推荐系统、大数据与智能计算
李林昊（1989—），男，山东威海人，讲师，博士，CCF会员，主要研究方向：智能监控、知识追踪
董瑶（1982—），女，河北唐山人，高级实验师，博士研究生，CCF会员，主要研究方向：知识图谱、数据挖掘。
基金资助:
国家自然科学基金资助项目(61902106);天津市自然科学基金资助项目(19JCZDJC40000);北航北斗技术成果转化及产业化资金资助项目(BARI2001);河北省高等学校科学技术研究项目(QN2021213)

Abstract

Abstract:

In order to solve the problem that the algorithms of publication venue recommendation always consider the text topics or the author’s history of publications separately， which leads to the low accuracy of publication venue recommendation results， a contribution recommendation algorithm of academic journal based on author preferences was proposed. In this algorithm， not only the text topics and the author’s history of publications were used together， but also the potential relationship between the academic focuses of publication venues and time were explored. Firstly， the Latent Dirichlet Allocation （LDA） topic model was used to extract the topic information of the paper title. Then， the topic-journal and time-journal model diagrams were established， and the Large-scale Information Network Embedding （LINE） model was used to learn the embedding of graph nodes. Finally， the author’s subject preferences and history of publication records were fused to calculate the journal composite scores， and the publication venue recommendation for author to contribute was realized. Experimental results on two public datasets， DBLP and PubMed， show that the proposed algorithm has better recall under different list lengths of recommended publication venues compared to six algorithms such as Singular Value Decomposition （SVD）， DeepWalk and Non-negative Matrix Factorization （NMF）. The proposed algorithm maintains high accuracy while requiring less information from papers and knowledge bases， and can effectively improve the robustness of publication venue recommendation algorithm.

Key words: academic journal, bipartite graph, publication venue recommendation, graph embedding, author preference

摘要：

针对投稿刊物推荐算法总是单独考虑文本主题或者作者历史发刊记录，导致投稿刊物推荐结果准确率低的问题，提出了一种基于作者偏好的学术刊物投稿推荐算法。该算法不仅协调使用了文本主题和作者历史发刊记录，还挖掘了投稿刊物的学术焦点与时间的潜在联系。首先，使用潜在狄利克雷（LDA）主题模型对文章标题进行主题提取；其次，建立主题-刊物和时间-刊物的模型图，并采用大规模信息网络嵌入（LINE）模型学习异构图节点的嵌入；最后，融合作者的主题偏好和历史发刊记录来计算刊物的综合得分，并据此对投稿作者进行投稿刊物推荐。在两个公开数据集DBLP和PubMed上的实验结果表明，相比奇异值分解（SVD）、DeepWalk、非负矩阵分解（NMF）等6个算法，所提出的算法在不同推荐的投稿刊物列表长度的情况下的召回率均为最优，并且在需要从论文和知识库中获取更少信息的同时，保持了较高的准确性，能有效提高投稿刊物推荐算法的鲁棒性。

关键词: 学术刊物, 二部图, 投稿推荐, 图嵌入, 作者偏好

CLC Number:

TP301.6

Yongfeng DONG, Xiangqian QU, Linhao LI, Yao DONG. Academic journal contribution recommendation algorithm based on author preferences[J]. Journal of Computer Applications, 2022, 42(1): 50-56.

董永峰, 屈向前, 李林昊, 董瑶. 基于作者偏好的学术投稿刊物推荐算法[J]. 《计算机应用》唯一官方网站, 2022, 42(1): 50-56.

Figures/Tables 6

References 18

1	LEWALLEN L P， CRANE P B. Choosing a publication venue［J］. Journal of Professional Nursing， 2010， 26（4）： 250-254. 10.1016/j.profnurs.2009.12.005
2	MCKERCHER B. Why and where to publish［J］. Tourism Management， 2015， 51： 306-308. 10.1016/j.tourman.2015.05.012
3	王永贵，宋真真，肖成龙. 基于改进聚类和矩阵分解的协同过滤推荐算法［J］. 计算机应用， 2018， 38（4）： 1001-1006. 10.11772/j.issn.1001-9081.2017092314
	WANG Y G， SONG Z Z， XIAO C L. Collaborative filtering recommendation algorithm based on improved clustering and matrix factorization［J］. Journal of Computer Applications， 2018， 38（4）： 1001-1006. 10.11772/j.issn.1001-9081.2017092314
4	张萌，南志红，基于用户偏好的信任网络随机游走推荐模型［J］. 计算机应用， 2016， 36（12）： 3363-3368. 10.11772/j.issn.1001-9081.2016.12.3363
	ZHANG M， NAN Z H. Trust network random walk model based on user preferences［J］. Journal of Computer Applications， 2016， 36（12）： 3363-3368. 10.11772/j.issn.1001-9081.2016.12.3363
5	YANG Z H， DAVISON B D. Venue recommendation： submitting your paper with style［C］// Proceedings of the 11th International Conference on Machine Learning and Applications. Piscataway： IEEE， 2012： 681-686. 10.1109/icmla.2012.127
6	MEDVET E， BARTOLI A， PICCININ G. Publication venue recommendation based on paper abstract［C］// Proceedings of the IEEE 26th International Conference on Tools with Artificial Intelligence. Piscataway： IEEE， 2014：1004-1010. 10.1109/ictai.2014.152
7	DEHDARIRAD H， GHAZIMIRSAEID J， JALALIMANESH A. Scholarly publication venue recommender systems： a systematic literature review ［J］. Data Technologies and Applications， 2020， 54（2）： 169-191. 10.1108/dta-08-2019-0135
8	DAI T， ZHU L， WANG Y X， et al. Attentive stacked denoising autoencoder with Bi-LSTM for personalized context-aware citation recommendation［J］. IEEE/ACM Transactions on Audio， Speech， and Language Processing， 2019， 28： 553-568. 10.1109/taslp.2019.2949925
9	LUONG H， HUYNH T， GAUCH S， et al. Publication venue recommendation using author network’s publication history［C］// Proceedings of the 2012 Asian Conference on Intelligent Information and Database Systems， LNCS7198. Berlin： Springer， 2012： 426-435.
10	PHAM M C， CAO Y W， KLAMMA R， et al. A clustering approach for collaborative filtering recommendation using social network analysis［J］. Journal of Universal Computer Science， 2011， 17（4）： 583-604. 10.1007/s13278-011-0024-x
11	CHEN Z， XIA F， JIANG H Z， et al. AVER： random walk based academic venue recommendation［C］// Proceedings of the 2015 24th International Conference on World Wide Web Companion. New York： ACM， 2015： 579-584. 10.1145/2740908.2741738
12	YU S， LIU J Y， YANG Z， et al. PAVE： Personalized academic venue recommendation exploiting co-publication networks［J］. Journal of Network and Computer Applications， 2018， 104： 38-47. 10.1016/j.jnca.2017.12.004
13	XIA F， ASABERE N Y， RODRIGUES J J P C， et al. Socially-aware venue recommendation for conference participants［C］// Proceedings of the IEEE 10th International Conference on Ubiquitous Intelligence and Computing and IEEE 10th International Conference on Autonomic and Trusted Computing. Piscataway： IEEE， 2013：134-141. 10.1109/uic-atc.2013.81
14	PRADHAN T， PAL S. A hybrid personalized scholarly venue recommender system integrating social network analysis and contextual similarity［J］. Future Generation Computer Systems， 2020， 110： 1139-1166. 10.1016/j.future.2019.11.017
15	ALSHAREEF A M， ALHAMID M F， SADDIK A EL. Academic venue recommendations based on similarity learning of an extended nearby citation network［J］. IEEE Access， 2019， 7： 38813-38825. 10.1109/access.2019.2906106
16	TANG J， QU M， WANG M， et al. LINE： large-scale information network embedding［C］// Proceedings of the 24th International Conference on World Wide Web. Republic and Canton of Geneva： International World Wide Web Conferences Steering Committee， 2015： 1067-1077. 10.1145/2736277.2741093
17	XIE M， YIN H Z， WANG H， et al. Learning graph-based POI embedding for location-based recommendation［C］// Proceedings of the 25th ACM International Conference on Information and Knowledge Management. New York： ACM， 2016： 15-24. 10.1145/2983323.2983711
18	HU X J， XU J J， WANG W Q， et al. A graph embedding based model for fine-grained POI recommendation［J］. Neurocomputing， 2021， 428： 376-384. 10.1016/j.neucom.2020.01.118

年份	DBLP数据集			PubMed数据集
年份	刊物数量	作者数量	文章数量	刊物数量	作者数量	文章数量
2009	1 200	130 573	66 083	968	86 492	35 441
2010	1 278	143 764	70 161	1 023	94 871	42 130
2011	1 340	158 071	80 615	1 136	105 647	42 681
2012	1 359	160 943	86 004	1 088	98 730	45 763
2013	1 379	172 381	91 546	1 176	135 864	50 608
2014	1 408	196 120	92 547	1 095	146 879	52 149
2015	1 431	206 328	94 252	1 184	112 687	49 360
2016	1 439	223 987	100 069	1 251	126 795	51 203
2017	1 431	258 829	108 475	1 231	153 897	53 455
2018	1 403	280 016	122 622	1 068	166 909	51 037
2019	1 407	275 314	122 739	1 044	143 050	48 576

年份	DBLP数据集			PubMed数据集
年份	刊物数量	作者数量	文章数量	刊物数量	作者数量	文章数量
2009	1 200	130 573	66 083	968	86 492	35 441
2010	1 278	143 764	70 161	1 023	94 871	42 130
2011	1 340	158 071	80 615	1 136	105 647	42 681
2012	1 359	160 943	86 004	1 088	98 730	45 763
2013	1 379	172 381	91 546	1 176	135 864	50 608
2014	1 408	196 120	92 547	1 095	146 879	52 149
2015	1 431	206 328	94 252	1 184	112 687	49 360
2016	1 439	223 987	100 069	1 251	126 795	51 203
2017	1 431	258 829	108 475	1 231	153 897	53 455
2018	1 403	280 016	122 622	1 068	166 909	51 037
2019	1 407	275 314	122 739	1 044	143 050	48 576

算法	DBLP数据集					PubMed数据集
算法	Recall@1	Recall@5	Recall@10	Recall@20	Recall@30	Recall@1	Recall@5	Recall@10	Recall@20	Recall@30
SVD	11.52	19.62	22.55	29.54	30.12	10.18	16.64	20.51	26.15	28.06
SVD++	15.87	16.92	27.09	30.15	31.54	12.20	16.84	22.21	25.85	30.08
KNN	0.08	2.98	36.78	37.12	39.52	0.07	2.77	22.65	29.12	33.64
CF	3.77	13.12	20.25	27.05	31.54	2.89	15.56	22.28	30.35	35.91
DeepWalk	28.17	55.74	66.31	69.98	76.15	27.36	48.13	55.69	59.14	63.12
NMF	22.15	27.56	30.50	53.64	58.31	19.50	25.43	28.15	56.41	59.84
AP-AJCR	30.12	58.62	75.05	78.15	81.33	27.40	49.99	58.10	60.03	65.33

算法	DBLP数据集					PubMed数据集
算法	Recall@1	Recall@5	Recall@10	Recall@20	Recall@30	Recall@1	Recall@5	Recall@10	Recall@20	Recall@30
SVD	11.52	19.62	22.55	29.54	30.12	10.18	16.64	20.51	26.15	28.06
SVD++	15.87	16.92	27.09	30.15	31.54	12.20	16.84	22.21	25.85	30.08
KNN	0.08	2.98	36.78	37.12	39.52	0.07	2.77	22.65	29.12	33.64
CF	3.77	13.12	20.25	27.05	31.54	2.89	15.56	22.28	30.35	35.91
DeepWalk	28.17	55.74	66.31	69.98	76.15	27.36	48.13	55.69	59.14	63.12
NMF	22.15	27.56	30.50	53.64	58.31	19.50	25.43	28.15	56.41	59.84
AP-AJCR	30.12	58.62	75.05	78.15	81.33	27.40	49.99	58.10	60.03	65.33

算法	DBLP数据集		PubMed数据集
算法	训练耗时	测试耗时	训练耗时	测试耗时
SVD	3 396.2	354.2	2 896.5	293.0
SVD++	26 544.1	347.8	20 544.9	295.2
KNN	5 291.6	405.6	5 090.5	367.6
CF	3 237.5	322.8	3 035.5	244.3
DeepWalk	398 015.8	2 642.3	342 201.2	2 258.6
NMF	29 201.0	2062.7	21 231.3	1 613.8
AP-AJCR	36 951.6	3 077.5	30 145.8	2 435.5

Academic journal contribution recommendation algorithm based on author preferences

基于作者偏好的学术投稿刊物推荐算法

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