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A novel hybrid publication recommendation system using compound information

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Abstract

Publication recommendation is an interesting but challenging research problem. Most existing studies only use partial information of papers’ contents, reference network or co-author relationship, which leads to an unsatisfied recommendation result. In this study, we propose a novel hybrid publication recommendation approach using compound information which retrieves top-K most relevant papers from a publication depository for a set of user input keywords. Our advantages comparing to the existing methods include: (1) Reaching a better recommendation results by taking the advantages of both content-based recommendation and citation-based recommendation and exploring much richer information of papers in one method; (2) Effectively solving the cold-start problem for new published papers by considering the vitality of papers and the impact factor of venues into the citation network; (3) Saving a large overhead in calculating the content-based similarity between papers and user input keywords by doing paper clustering based on the citation network. Extensive experiments on DBLP and Microsoft Academic datasets demonstrate that PubTeller improves the state-of-the-art methods with 4% in Precision and 4.5% in Recall.

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Notes

  1. http://scholar.google.com

  2. https://en.wikipedia.org/wiki/Impact_factor

  3. http://arnetminer.org/DBLP_Citation

  4. http://academic.research.microsoft.com

References

  1. Amami, M., Pasi, G., Stella, F., Faiz, R.: An Lda-Based approach to scientific paper recommendation. In: International Conference on Applications of Natural Language to Information Systems, pp. 200–210. Springer (2016)

  2. Beel, J., Langer, S., Genzmehr, M., Gipp, B., Breitinger, C., Nürnberger, A.: Research paper recommender system evaluation: a quantitative literature survey. In: Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation, pp. 15–22. ACM (2013)

  3. Belkin, N.J., Croft, W.B.: Information filtering and information retrieval: two sides of the same coin? Commun. ACM 35(12), 29–38 (1992)

    Article  Google Scholar 

  4. Cazella, S.C., Alvares, L.O.C.: An architecture based on multi-agent system and data mining for recommending research papers and researchers. In: Eighteenth International Conference on Software Engineering & Knowledge Engineering, pp. 67–72 (2006)

  5. Chen, J., Tang, Y., Li, J., Mao, C., Xiao, J.: Community-based scholar recommendation modeling in academic social network sites. In: International Conference on Web Information Systems Engineering, pp. 325–334. Springer (2013)

  6. Ekstrand, M.D., Kannan, P., Stemper, J.A., Butler, J.T., Konstan, J.A., Riedl, J.T.: Automatically building research reading lists. In: Proceedings of the fourth ACM conference on Recommender systems, pp. 159–166. ACM (2010)

  7. Gori, M., systems, A. Pucci.: Research paper recommender a random-walk based approach. In: 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI’06), pp 778–781. IEEE (2006)

  8. He, Q., Pei, J., Kifer, D., Mitra, P., Giles, L.: Context-aware citation recommendation. In: Proceedings of the 19th international conference on World wide Web, pp. 421–430. ACM (2010)

  9. Huang, W., Wu, Z., Chen, L., Mitra, P., Giles, C.L.: A neural probabilistic model for context based citation recommendation. In: AAAI, pp. 2404–2410 (2015)

  10. Huang, Z., Qiu, Y.: A multiple-perspective approach to constructing and aggregating citation semantic link network. Futur. Gener. Comput. Syst. 26(3), 400–407 (2010)

    Article  Google Scholar 

  11. Huang, Z., Zeng, D., Chen, H.: A comparison of collaborative-filtering recommendation algorithms for e-commerce. Intelligent Systems IEEE 22(5), 68–78 (2007)

    Article  Google Scholar 

  12. Küçüktunç, O., Saule, E., Kaya, K., Çatalyürek, U.̈ V.: Direction awareness in citation recommendation. Wien. Med. Wochenschr. 123(9), 148–149 (2012)

    Google Scholar 

  13. Küçüktunç, O., Saule, E., Kaya, K., Çatalyürek, U.̈V.: Recommendation on academic networks using direction aware citation analysis. arXiv:1205.1143 (2012)

  14. Lang, K.: Newsweeder: Learning to filter news (1995)

  15. Lao, N., Cohen, W.W.: Relational retrieval using a combination of path-constrained random walks. Mach. Learn. 81(1), 53–67 (2010)

    Article  MathSciNet  Google Scholar 

  16. Lee, J., Lee, K., Kim, J.G.: Personalized academic research paper recommendation system. Computer Science (2013)

  17. Li, J., Willett, P.: Articlerank: a pagerank-based alternative to numbers of citations for analysing citation networks. In: Aslib Proceedings, vol. 61, pp. 605–618. Emerald Group Publishing Limited (2009)

  18. Li, J., Xia, F., Wang, W., Chen, Z., Asabere, N.Y., Jiang, H.: Acrec: a co-authorship based random walk model for academic collaboration recommendation. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 1209–1214. ACM (2014)

  19. Liang, Y., Li, Q., Qian, T.: Finding relevant papers based on citation relations. In: International Conference on Web-Age Information Management, pp. 403–414. Springer (2011)

  20. Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)

    Article  Google Scholar 

  21. Lopes, G.R., Moro, M.M., Wives, L.K., De Oliveira, J.P.M.: Collaboration recommendation on academic social networks. In: International Conference on Conceptual Modeling, pp. 190–199. Springer (2010)

  22. Lu, M., Wei, X., Gao, J., Shi, Y.: Ahits-upt: A high quality academic resources recommendation method. In: IEEE International Conference on Smart City/Socialcom/Sustaincom, pp. 507–512 (2015)

  23. Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Forth International Conference on Web Search and Web Data Mining, WSDM 2011, pp. 287–296. Hong Kong (2011)

  24. Ma, K., Lu, T., Abraham, A.: Hybrid Parallel Approach for Personalized Literature Recommendation System. In: International Conference on Computational Aspects of Social Networks, pp. 31–36 (2014)

  25. Ma, N., Guan, J., Zhao, Y.: Bringing pagerank to the citation analysis. Inf. Process. Manag. 44(2), 800–810 (2008)

    Article  Google Scholar 

  26. Massa, P., Avesani, P.: Trust-Aware Collaborative Filtering for Recommender Systems. Springer, Berlin (2004)

    Book  Google Scholar 

  27. Mcnee, S.M., Albert, I., Dan, C., Gopalkrishnan, P., Lam, S.K., Rashid, A.M., Konstan, J.A., Riedl, J.: On the recommending of citations for research papers. In: Cscw02, P, pp. 116–125 (2003)

  28. Nallapati, R.M., Ahmed, A., Xing, E.P., Cohen, W.W.: Joint latent topic models for text and citations. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 542–550. ACM (2008)

  29. Newman, M.E.: Scientific collaboration networks. i. network construction and fundamental results. Phys. Rev. E 64(64), 016131 (2001)

    Article  Google Scholar 

  30. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the Web (1999)

  31. Ren, X., Liu, J., Yu, X., Khandelwal, U., Gu, Q., Wang, L., Han, J.: Cluscite: Effective citation recommendation by information network-based clustering. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pp. 821–830. ACM (2014)

  32. Salton, G.: Associative document retrieval techniques using bibliographic information. J. ACM (JACM) 10(4), 440–457 (1963)

    Article  Google Scholar 

  33. Sun, L., Franklin, M.J., Krishnan, S., Xin, R.S.: Fine-grained partitioning for aggressive data skipping. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 1115–1126. ACM (2014)

  34. Tang, J., Zhang, J.: A Discriminative Approach to Topic-Based Citation Recommendation. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp 572–579. Springer (2009)

  35. Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: Arnetminer: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 990–998. ACM (2008)

  36. Torres, R., McNee, S.M., Abel, M., Konstan, J.A., Riedl, J.: Enhancing digital libraries with techlens+. In: Proceedings of the 4th ACM/IEEE-CS Joint Conference on Digital libraries, pp. 228–236. ACM (2004)

  37. Wang, Q., Li, W., Zhang, X., Lu, S.: Academic paper recommendation based on community detection in citation-collaboration networks (2016)

    Chapter  Google Scholar 

  38. Wang, Y., Zhai, E., Hu, J., Claper, Z. Chen.: Recommend classical papers to beginners. In: Seventh International Conference on Fuzzy Systems and Knowledge Discovery, pp. 2777–2781 (2010)

  39. Yang, Z., Yin, D., Davison, B.D.: Recommendation in academia a joint multi-relational model. Ieee/Acm International Conference on Advances in Social Networks Analysis and Mining, pp. 566–571 (2014)

  40. Zhang, P.Y., Du, Y.J., Wang, C.: A hybrid method based on hits for literature recommendation. Appl. Mech. Mater. 55-57, 1636–1641 (2011)

    Article  Google Scholar 

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Acknowledgements

This research is partially supported by National Natural Science Foundation of China (No. 61632016, 61572336, 61572335, 61772356), the Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003, 18KJA520010), the Open Program of Neusoft Corporation (No. SKLSAOP1801), and King Abdullah University of Science and Technology (KAUST) under award number FCC/1/1976-19-01.

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Correspondence to Zhixu Li.

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This article belongs to the Topical Collection: Special Issue on Web and Big Data

Guest Editors: Junjie Yao, Bin Cui, Christian S. Jensen, and Zhe Zhao

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Yang, Q., Li, Z., Liu, A. et al. A novel hybrid publication recommendation system using compound information. World Wide Web 22, 2499–2517 (2019). https://doi.org/10.1007/s11280-019-00687-9

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