Publication Venue Recommendation Based on Paper’s Title and Co-authors Network
Subject Areas : Decision support systemsRamin Safa 1 , Seyed Abolghassem Mirroshandel 2 * , Soroush Javadi 3 , Mohammad Azizi 4
1 - University of Guilan
2 - University of Guilan
3 - University of Guilan
4 - University of Guilan
Keywords: Academic Recommender Systems , Social Network Analysis , Venue Recommendation , DBLP,
Abstract :
Information overload has always been a remarkable topic in scientific researches, and one of the available approaches in this field is employing recommender systems. With the spread of these systems in various fields, studies show the need for more attention to applying them in scientific applications. Applying recommender systems to scientific domain, such as paper recommendation, expert recommendation, citation recommendation and reviewer recommendation, are new and developing topics. With the significant growth of the number of scientific events and journals, one of the most important issues is choosing the most suitable venue for publishing papers, and the existence of a tool to accelerate this process is necessary for researchers. Despite the importance of these systems in accelerating the publication process and decreasing possible errors, this problem has been less studied in related works. So in this paper, an efficient approach will be suggested for recommending related conferences or journals for a researcher’s specific paper. In other words, our system will be able to recommend the most suitable venues for publishing a written paper, by means of social network analysis and content-based filtering, according to the researcher’s preferences and the co-authors’ publication history. The results of evaluation using real-world data show acceptable accuracy in venue recommendations.
[1] F. Ricci, L. Rokach and B. Shapira (2011). Introduction to recommender systems handbook. Springer.
[2] D. Jannach, M. Zanker, A. Felfernig and G. Friedrich (2010). Recommender systems: An introduction. Cambridge University Press.
[3] J. Bobadilla, F. Ortega, A. Hernando and A. Gutiérrez (2013). Recommender systems survey. Knowledge-Based Systems 46:109–132.
[4] X. Su and T. M. Khoshgoftaar (2009). A survey of collaborative filtering techniques. Advances in Artificial Intelligence 2009:4.
[5] J. Beel, B. Gipp, S. Langer and C. Breitinger (2016). Paper recommender systems: A literature survey. International Journal on Digital Libraries 17(4):305–338.
[6] C. Pan and W. Li (2010). Research paper recommendation with topic analysis. In: Computer Design and Applications (ICCDA), 2010 International Conference on, IEEE, vol 4, pp V4–264.
[7] H. Luong, T. Huynh, S. Gauch, L. Do and K. Hoang (2012). Publication venue recommendation using author network’s publication history. In: Asian Conference on Intelligent Information and Database Systems, Springer, pp 426–435.
[8] T. Huynh, K. Hoang and D. Lam (2013). Trend based vertex similarity for academic collaboration recommendation. In: International Conference on Computational Collective Intelligence, Springer, pp 11–20.
[9] C. Bancu, M. Dagadita, M. Dascalu, C. Dobre, S. Trausan-Matu and A. M. Florea (2012). ARSYS–article recommender system. In: Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2012 14th International Symposium on, IEEE, pp 349–355.
[10] M. Gori and A. Pucci (2006). Research paper recommender systems: A random-walk based approach. In: 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings) (WI’06), IEEE, pp 778–781.
[11] Y. Jiang, A. Jia, Y. Feng and D. Zhao (2012). Recommending academic papers via users’ reading purposes. In: Proceedings of the sixth ACM conference on Recommender systems, ACM, pp 241–244.
[12] J. Sun, J. Ma, X. Liu, Z. Liu, G. Wang, H. Jiang and T. Silva (2013). A novel approach for personalized article recommendation in online scientific communities. In: System Sciences (HICSS), 2013 46th Hawaii International Conference on, IEEE, pp 1543–1552.
[13] G. Cabanac (2011). Accuracy of inter-researcher similarity measures based on topical and social clues. Scientometrics 87(3):597–620.
[14] M. S. Pera and Y. K. Ng (2014). Exploiting the wisdom of social connections to make personalized recommendations on scholarly articles Journal of Intelligent Information Systems 42(3):371–391.
[15] Z. Zhan, L. Yang, S. Bao, D. Han, Z. Su and Y. Yu (2011). Finding appropriate experts for collaboration. In: International Conference on Web-Age Information Management, Springer, pp 327–339.
[16] C. Basu, H. Hirsh, W. W. Cohen and C. Nevill-Manning (2001). Technical paper recommendation: A study in combining multiple information sources. Journal of Artificial Intelligence Research 14:241–262.
[17] D. Conry, Y. Koren and N. Ramakrishnan (2009). Recommender systems for the conference paper assignment problem. In: Proceedings of the third ACM conference on Recommender systems, ACM, pp 357–360.
[18] B. Gipp and J. Beel (2009). Citation proximity analysis (CPA)–a new approach for identifying related work based on co-citation analysis. In: Proceedings of the 12th International Conference on Scientometrics and Informetrics (ISSI’09), Rio de Janeiro (Brazil): International Society for Scientometrics and Informetrics, vol 2, pp 571–575.
[19] C. Caragea, A. Silvescu, P. Mitra and C. L. Giles (2013). Can’t see the forest for the trees?: A citation recommendation system. In: Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries, ACM, pp 111–114.
[20] R. Klamma, P. M. Cuong and Y. Cao (2009). You never walk alone: Recommending academic events based on social network analysis. In: International Conference on Complex Sciences, Springer, pp 657–670.
[21] D. H. Park, H. K. Kim, I. Y. Choi and J. K. Kim (2012). A literature review and classification of recommender systems research. Expert Systems with Applications 39(11):10,059–10,072.
[22] G. H. Martı́n, S. Schockaert, C. Cornelis and H. Naessens (2013). An exploratory study on content-based filtering of call for papers. In: Information Retrieval Facility Conference, Springer, pp 58–69.
[23] E. Medvet, A. Bartoli and G. Piccinin (2014). Publication venue recommendation based on paper abstract. In: Tools with Artificial Intelligence (ICTAI), 2014 IEEE 26th International Conference on, IEEE, pp 1004–1010.
[24] Z. Xu, Y. Yang, F. Wang, J. Xu, Z. Li, F. Mu and L. Li (2014). A recommendation system for paper submission based on vertical search engine. In: Computer Engineering and Networking, Springer, pp 201–208.
[25] F. Beierle, J. Tan and K. Grunert (2016). Analyzing social relations for recommending academic conferences. In: Proceedings of the 8th ACM International Workshop on Hot Topics in Planet-scale Mobile Computing and Online Social Networking, ACM, pp 37–42.
[26] G. M. Garcı́a, B. P. Nunes, G. R. Lopes, M. A. Casanova and L. A. P. P. Leme (2016). Comparing and recommending conferences. In: Proceedings of the 5th Bra, SNAM–Brazilian Workshop on Social Network Analysis and Mining.
[27] D. Peiris and R. Weerasinghe (2015). Citation network based framework for ranking academic publications and venues. In: Advances in ICT for Emerging Regions (ICTer), 2015 Fifteenth International Conference on, IEEE, pp 146–151.
[28] S. Perugini, M. A. Gonçalves and E. A. Fox (2004). Recommender systems research: A connection-centric survey. Journal of Intelligent Information Systems 23(2):107–143.
[29] X. Ren, Y. Zeng, Y. Qin, N. Zhong, Z. Huang, Y. Wang and C. Wang (2010). Social relation based search refinement: Let your friends help you! In: International Conference on Active Media Technology, Springer, pp 475–485.
[30] Y. Zeng, Y. Yao and N. Zhong (2009). DBLP-SSE: A DBLP search support engine. In: Web Intelligence and Intelligent Agent Technologies (WI-IAT’09), IEEE/WIC/ACM International Joint Conferences on, IET, vol 1, pp 626–630.
[31] D. Tchuente, M. F. Canut, N. Jessel, A. Peninou and F. Sèdes (2013). A community-based algorithm for deriving users’ profiles from egocentrics networks: Experiment on Facebook and DBLP. Social Network Analysis and Mining 3(3):667–683.
[32] M. Eirinaki, J. Gao, I. Varlamis and K. Tserpes (2018). Recommender systems for large-scale social networks: A review of challenges and solutions.
[33] H. C. Wang and Y. L. Chang (2007). PKR: A personalized knowledge recommendation system for virtual research communities. Journal of Computer Information Systems 48(1):31–41.
[34] J. B. Lovins (1968). Development of a stemming algorithm. MIT Information Processing Group, Electronic Systems Laboratory Cambridge.
[35] C. D. Manning, P. Raghavan and H. Schtze (2008). Introduction to information retrieval. Cambridge University Press.
[36] T. K. Landauer and S. T. Dumais (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review 104(2):211.
[37] B. Sarwar, G. Karypis, J. Konstan and J. Riedl (2000). Application of dimensionality reduction in recommender system–a case study. Tech. rep., DTIC Document.
[38] M. Ley (2009). DBLP: some lessons learned. Proceedings of the VLDB Endowment 2(2):1493–1500.