ISPREC++: Learning Edge Type Importance in Network-Oriented Paper Recommendation

Document Type : Research Article


1 Faculty of new sciences and technologies, University of Tehran, Tehran, Iran

2 Faculty of Mathematical sciences, Alzahra University, Tehran, Iran


With the spread of the Internet and the possibility of online access to articles, a wide range of scientific articles are available to researchers, while finding relevant articles among this substantial number of articles turns out to be a real dilemma. To solve this problem, several scientific paper recommendation algorithms have been proposed. Most of these algorithms suffer from some drawbacks that limit their usability. For example, many of these recommendation methods are designed to recommend papers only to users who had published articles before and can’t support new researchers. Also, they usually do not utilize many important features of articles each of which can have a role in determining the relevance of the articles to users. To address these concerns, in this paper, we present the novel method of Integrated Scientific Paper Recommendation with an edge-weight learning approach, called ISPREC++, as an extended version of ISPREC that focuses on learning the weights of edge types in Heterogeneous Information Networks based on users' preferences. ISPREC++ sets the weights of edges in SPIN using a Bayesian Personalized Ranking (BPR) based method and utilizes Gradient Descent to optimize its objective function. Thereafter, it exploits a limited random-walk algorithm for a Top-N recommendation. Extensive experiments on a real-world dataset demonstrate the significant performance superiority of ISPREC++ compared to the state-of-the-art scientific paper recommendation algorithms.


Main Subjects

[1] X. Bai, M. Wang, I. Lee, Z. Yang, X. Kong, F. Xia, Scientific paper recommendation: A survey, Ieee Access, 7 (2019) 9324-9339.
[2] J. Sun, J. Ma, Z. Liu, Y. Miao, Leveraging content and connections for scientific article recommendation in social computing contexts, The Computer Journal, 57(9) (2014) 1331-1342.
[3] M.S. Pera, Y.-K. Ng, Exploiting the wisdom of social connections to make personalized recommendations on scholarly articles, Journal of Intelligent Information Systems, 42(3) (2014) 371-391.
[4] K. Sugiyama, M.-Y. Kan, Serendipitous recommendation for scholarly papers considering relations among researchers, in:  Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries, 2011, pp. 307-310.
[5] F. Xia, H. Liu, I. Lee, L. Cao, Scientific article recommendation: Exploiting common author relations and historical preferences, IEEE Transactions on Big Data, 2(2) (2016) 101-112.
[6] W. Zhao, R. Wu, H. Liu, Paper recommendation based on the knowledge gap between a researcher's background knowledge and research target, Information processing & management, 52(5) (2016) 976-988.
[7] S. Alotaibi, J. Vassileva, Personalized Recommendation of Research Papers by Fusing Recommendations from Explicit and Implicit Social Network, in:  UMAP (Extended Proceedings), 2016.
[8] Q. Wang, W. Li, X. Zhang, S. Lu, Academic paper recommendation based on community detection in citation-collaboration networks, in:  Asia-Pacific web conference, Springer, 2016, pp. 124-136.
[9] X. Ma, R. Wang, Personalized scientific paper recommendation based on heterogeneous graph representation, IEEE Access, 7 (2019) 79887-79894.
[10] E. Jafari, B. Shams, S. Haratizadeh, ISPREC: Integrated Scientific Paper Recommendation using heterogeneous information network, in:  2021 12th International Conference on Information and Knowledge Technology (IKT), IEEE, 2021, pp. 112-118.
[11] X. Ma, Y. Zhang, J. Zeng, Newly published scientific papers recommendation in heterogeneous information networks, Mobile Networks and Applications, 24(1) (2019) 69-79.
[12] K. Haruna, M.A. Ismail, A.B. Bichi, V. Chang, S. Wibawa, T. Herawan, A citation-based recommender system for scholarly paper recommendation, in:  International Conference on Computational Science and Its Applications, Springer, 2018, pp. 514-525.
[13] N. Sakib, R.B. Ahmad, K. Haruna, A collaborative approach toward scientific paper recommendation using citation context, IEEE Access, 8 (2020) 51246-51255.
[14] J. Son, S.B. Kim, Academic paper recommender system using multilevel simultaneous citation networks, Decision Support Systems, 105 (2018) 24-33.
[15] W. Waheed, M. Imran, B. Raza, A.K. Malik, H.A. Khattak, A hybrid approach toward research paper recommendation using centrality measures and author ranking, IEEE Access, 7 (2019) 33145-33158.
[16] G. Guo, B. Chen, X. Zhang, Z. Liu, Z. Dong, X. He, Leveraging title-abstract attentive semantics for paper recommendation, in:  Proceedings of the AAAI conference on artificial intelligence, 2020, pp. 67-74.
[17] L. Berkani, R. Hanifi, H. Dahmani, Hybrid recommendation of articles in scientific social networks using optimization and multiview clustering, in:  International Conference on Smart Applications and Data Analysis, Springer, 2020, pp. 117-132.
[18] G. Wang, X. He, C.I. Ishuga, HAR-SI: A novel hybrid article recommendation approach integrating with social information in scientific social network, Knowledge-Based Systems, 148 (2018) 85-99.
[19] M. Alfarhood, J. Cheng, CATA++: A collaborative dual attentive autoencoder method for recommending scientific articles, IEEE Access, 8 (2020) 183633-183648.
[20] T. Cai, H. Cheng, J. Luo, S. Zhou, An efficient and simple graph model for scientific article cold start recommendation, in:  International Conference on Conceptual Modeling, Springer, 2016, pp. 248-259.
[21] W. Liu, L. Lü, Link prediction based on local random walk, EPL (Europhysics Letters), 89(5) (2010) 58007.
[22] N. Lao, W.W. Cohen, Fast query execution for retrieval models based on path-constrained random walks, in:  Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010, pp. 881-888.
[23] C. Shi, X. Kong, Y. Huang, S.Y. Philip, B. Wu, Hetesim: A general framework for relevance measure in heterogeneous networks, IEEE Transactions on Knowledge and Data Engineering, 26(10) (2014) 2479-2492.
[24] S. Lee, S. Lee, B.-H. Park, Pathmining: A path-based user profiling algorithm for heterogeneous graph-based recommender systems, in:  The Twenty-Eighth International Flairs Conference, 2015.
[25] L. Page, S. Brin, R. Motwani, T. Winograd, The PageRank citation ranking: Bringing order to the web, Stanford InfoLab, 1999.
[26] T.H. Haveliwala, Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search, IEEE transactions on knowledge and data engineering, 15(4) (2003) 784-796.
[27] A. Balmin, V. Hristidis, Y. Papakonstantinou, Objectrank: Authority-based keyword search in databases, in:  VLDB, 2004, pp. 564-575.
[28] M. Gori, A. Pucci, V. Roma, I. Siena, Itemrank: A random-walk based scoring algorithm for recommender engines, in:  IJCAI, 2007, pp. 2766-2771.
[29] S. Lee, S. Park, M. Kahng, S.-g. Lee, PathRank: Ranking nodes on a heterogeneous graph for flexible hybrid recommender systems, Expert Systems with Applications, 40(2) (2013) 684-697.
[30] Z. Jiang, H. Liu, B. Fu, Z. Wu, T. Zhang, Recommendation in heterogeneous information networks based on generalized random walk model and bayesian personalized ranking, in:  Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 2018, pp. 288-296.
[31] Y. Sun, J. Han, X. Yan, P.S. Yu, T. Wu, Pathsim: Meta path-based top-k similarity search in heterogeneous information networks, Proceedings of the VLDB Endowment, 4(11) (2011) 992-1003.
[32] N. Li, Y. Yu, Z.-H. Zhou, Diversity regularized ensemble pruning, in:  Joint European conference on machine learning and knowledge discovery in databases, Springer, 2012, pp. 330-345.
[33] C. Wang, D.M. Blei, Collaborative topic modeling for recommending scientific articles, in:  Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011, pp. 448-456.
[34] H. Liu, Z. Jiang, Y. Song, T. Zhang, Z. Wu, User preference modeling based on meta paths and diversity regularization in heterogeneous information networks, Knowledge-Based Systems, 181 (2019) 104784.
[35] R. Pan, Y. Zhou, B. Cao, N.N. Liu, R. Lukose, M. Scholz, Q. Yang, One-class collaborative filtering, in:  2008 Eighth IEEE International Conference on Data Mining, IEEE, 2008, pp. 502-511.
[36] S. Rendle, C. Freudenthaler, Z. Gantner, L. Schmidt-Thieme, BPR: Bayesian personalized ranking from implicit feedback, arXiv preprint arXiv:1205.2618,  (2012).
[37] S. Ruder, An overview of gradient descent optimization algorithms, arXiv preprint arXiv:1609.04747,  (2016).
[38] R. Ying, R. He, K. Chen, P. Eksombatchai, W.L. Hamilton, J. Leskovec, Graph convolutional neural networks for web-scale recommender systems, in:  Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, 2018, pp. 974-983.
[39] H. Wang, B. Chen, W.-J. Li, Collaborative topic regression with social regularization for tag recommendation, in:  Twenty-Third International Joint Conference on Artificial Intelligence, 2013.
[40] N. Chiluka, N. Andrade, J. Pouwelse, A link prediction approach to recommendations in large-scale user-generated content systems, in:  European Conference on Information Retrieval, Springer, 2011, pp. 189-200.
[41] Y. Shi, M. Larson, A. Hanjalic, Unifying rating-oriented and ranking-oriented collaborative filtering for improved recommendation, Information Sciences, 229 (2013) 29-39.