Research paper recommender systems on big scholarly data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 11016 LNAI, Issue , 2018, Pages 251-260

  Chen, Tsung Teng ^a   Lee, Maria ^b  

^a NATIONAL TAIPEI UNIVERSITY   (Taiwan)

^b SHIH CHIEN UNIVERSITY   (Taiwan)

Author keywords

Big Scholarly Data; Collaborative filtering; Recommender systems; Research paper recommender systems

Indexed keywords

COLLABORATIVE FILTERING; DATA REDUCTION; INTELLIGENT SYSTEMS; KNOWLEDGE ACQUISITION; KNOWLEDGE MANAGEMENT; PAPER; WINDOWS OPERATING SYSTEM;

BIG SCHOLARLY DATA; CITATION INFORMATION; COLD START PROBLEMS; COMPUTER CLUSTERS; ITEM-BASED COLLABORATIVE FILTERING; RESEARCH FIELDS; RESEARCH MANAGEMENT; RESEARCH PAPERS;

RECOMMENDER SYSTEMS;

EID: 85051131982     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-97289-3_20     Document Type: Conference Paper

References (20)

1. Beel, J., et al.: Research-paper recommender systems: a literature survey. Int. J. Digit. Libr. 17(4), 305–338 (2016)
2. Rich, E.: User modeling via stereotypes. Cogn. Sci. 3(4), 329–354 (1979)
3. Wu, Z., et al.: Towards building a scholarly big data platform: challenges, lessons and opportunities. In: Proceedings of the 14th ACM/IEEE-CS Joint Conference on Digital Libraries, London, United Kingdom, pp. 117–126. IEEE Press (2014)
4. Khan, S., et al.: A survey on scholarly data: From big data perspective. Inf. Process. Manag. 53(4), 923–944 (2017)
5. Xia, F., et al.: Big scholarly data: a survey. IEEE Trans. Big Data 3(1), 18–35 (2017)
6. Sesagiri Raamkumar, A., Foo, S., Pang, N.: Using author-specified keywords in building an initial reading list of research papers in scientific paper retrieval and recommender systems. Inf. Process. Manag. 53(3), 577–594 (2017)
7. Haruna, K., et al.: A collaborative approach for research paper recommender system. PLoS ONE 12(10), e0184516 (2017)
8. Ismail, A.S., Al-Feel, H.: Digital library recommender system on Hadoop. In: Proceedings of the 2015 IEEE 4th Symposium on Network Cloud Computing and Applications, pp. 111– 114. IEEE Computer Society (2015)
9. Xia, F., et al.: Scientific article recommendation: exploiting common author relations and historical preferences. IEEE Trans. Big Data 2(2), 101–112 (2016)
10. Son, J., Kim, S.B.: Academic paper recommender system using multilevel simultaneous citation networks. Decis. Support Syst. 105, 24–33 (2018)
11. Sinha, A., et al.: An overview of microsoft academic service (MAS) and applications. In: Proceedings of the 24th International Conference on World Wide Web, Florence, Italy, pp. 243–246. ACM (2015)
12. Open Academic Society (2017). https://www.openacademic.ai/. Accessed 3 Jan 2018
13. Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, pp. 263– 272. IEEE Computer Society (2008)
14. Cano, V.: Citation behavior: classification, utility, and location. J. Am. Soc. Inf. Sci. 40(4), 284–290 (1989)
15. Lutz, B., Hans-Dieter, D.: What do citation counts measure? A review of studies on citing behavior. J. Doc. 64(1), 45–80 (2008)
16. McNee, S.M., et al.: On the recommending of citations for research papers. In: Proceedings of the 2002 ACM Conference on Computer Supported Cooperative Work, New Orleans, Louisiana, USA, pp. 116–125. ACM (2002)
17. Singh, D., Reddy, C.K.: A survey on platforms for big data analytics. J. Big Data 2(1), 8 (2014)
18. Zachariah, D., et al.: Alternating least-squares for low-rank matrix reconstruction. IEEE Signal Process. Lett. 19(4), 231–234 (2012)
19. Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems. ACM (2010)
20. Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, California, USA, pp. 448–456. ACM (2011)