Leveraging content and connections for scientific article recommendation in social computing contexts

Computer Journal

Volumn 57, Issue 9, 2014, Pages 1331-1342

  Sun, Jianshan ^a,b   Ma, Jian ^b   Liu, Zhiying ^a   Miao, Yajun ^a  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Article recommendation; Research social network; Semantic content expansion; Social computing application

Indexed keywords

SEMANTICS; SOCIAL NETWORKING (ONLINE); SOCIAL SCIENCES COMPUTING;

ARTICLE RECOMMENDATION; PERSONALIZED RECOMMENDATION; RECOMMENDATION TECHNIQUES; SCIENTIFIC ARTICLES; SCIENTIFIC INFORMATION; SEMANTIC CONTENT; SIMILARITY CALCULATION; SOCIAL COMPUTING APPLICATIONS;

SOCIAL SCIENCES COMPUTING; SOCIAL NETWORKING (ONLINE);

EID: 84906822916     PISSN: 00104620     EISSN: 14602067     Source Type: Journal    
DOI: 10.1093/comjnl/bxt086     Document Type: Article

References (44)

1. Jinha, A.E. (2010) Article 50 million: an estimate of the number of scholarly articles in existence. Learn. Publ., 23, 258-263.
2. Bogers,T. and Bosch,V.D. (2008) Recommending Scientific Articles Using Citeulike. Proc. RecSys 08, Lausanne, Switzerland, October 23-25, pp. 287-290. ACM, NewYork, NY, USA.
3. Parra, D. and P. Brusilovsky. (2009) Collaborative filtering for social tagging systems: an experiment with citeulike. Proceedings of RecSys 09, NewYork, NY, USA, October 22-25, pp. 237-240. ACM, NewYork, NY, USA.
4. Liang, T.P., Yang, Y.F., Chen, D.N. and Ku, Y.C. (2008) A semantic-expansion approach to personalized knowledge recommendation. Decis. Support Syst., 45, 401-412.
5. Bogers, T. and Bosch, V.D. (2009) Collaborative and Content-Based Filtering for Item Recommendation on Social Bookmarking Websites. RecSys'09Workshop on Recommender Systems and the SocialWeb, NewYork,NY, USA, October 25, pp. 9-16.ACM, NewYork, NY, USA.
6. Liang, T.P., Lai, H.J. and Ku, Y.C. (2007) Personalized content recommendation and user satisfaction: theoretical synthesis and empirical findings. J. Manage. Inf. Syst., 23, 45-70.
7. Pazzani, M. and Billsus, D. (2007) Content-based recommendation systems. Lect. Notes Comput. Sci., 4321, 325-341.
8. Schafer, J., Frankowski, D., Herlocker, J. and Sen, S. (2007) Collaborative filtering recommender systems. Lect. Notes Comput. Sci., 4321, 291-324.
9. White, R.W., Bailey, P. and Chen, L. (2009) Predicting User Interests from Contextual Information. Proc. SIGIR 09, Boston, Massachusetts, USA, July 19-23, pp. 363-370.ACM, NewYork, NY, USA.
10. Lv, Y., Moon, T., Kolari, P., Zheng, Z., Wang, X. and Chang, Y. (2011) Learning to Model Relatedness for News Recommendation. Proc.WWW11, Hyderabad, India, 28 March-1 April, pp. 57-66. ACM, NewYork, NY, USA.
11. Mooney, R.J. and Roy, L. (2000) Content-Based Book Recommending using Learning for Text Categorization. Proc DL 00, San Antonio, TX, USA, June 2-7, pp. 195-204. ACM, NewYork, NY, USA.
12. Hwang, S. and Chuang, S. (2004) Combining article content andWeb usage for literature recommendation in digital libraries. Online Inform. Rev., 28, 260-272.
13. McNee, S. (2006) Meeting user information needs in recommender systems. PhD Thesis, University of Minnesota
14. Kim, H.N., Ha, I., Lee, K.S., Jo, G.S. and EI, A. (2011) Collaborative user modeling for enhanced content filtering in recommender systems. Decis. Support Syst., 51, 772-781.
15. Vivacqua, A.S., Oliveira, J. and De Souza, J.M. (2009) i-ProSE: inferring user profiles in a scientific context. Comput. J., 52, 789-798.
16. Bollacker, K.D., Lawrence, S. and Giles, C.L. (2000) Discovering relevant scientific literature on theWeb. IEEE Intell. Syst. Appl., 15, 42-47.
17. Basu, C., Cohen,W.W., Hirsh, H. and Nevil-Manning, C. (2001) Technical paper recommendation: a study in combining multiple information sources. J. Artif. Intell. Res., 14, 231-252.
18. Cohen, W.W. (2000) WHIRL: a word-based information representation language. Artif. Intell., 118, 163-196
19. Kim, Y.S. (2011) Text recommender system using user's usage patterns. Ind. Manage. Data Syst., 111, 282-297.
20. Martin, G.H., Schochaert, S., Cornelis, C. and Naessens, H. (2013) Using semi-structured data for assessing research paper similarity. Inf. Sci., 221, 245-261.
21. Chandrasekaran, K., Gauch, S., Lakkaraju, P. and Luong, H. (2008) Concept-based document recommendations for citeSeer authors. Lect. Notes Comput. Sci., 5149, 83-92.
22. Vellino, A. (2010) A Comparison between Usage-Based and Citation-Based Methods for Recommending Scholarly Research Articles. Proc. ASIS& T 2010, Pittsburgh, PA, USA, October 22-27, pp. 1-2.Wiley, New York.
23. Pera, M.S. and Ng,Y.K. (2011)A Personalized Recommendation System on Scholarly Publications. Proc. CIKM 11, Glasgow, Scotland, UK, October 24-28, pp. 2133-2136.ACM, NewYork, NY, USA.
24. Lee, D.H. and Brusilovsky, P. (2010) Using Self-Defined Group Activities for Improving Recommendations in Collaborative Tagging Systems. Proc. RecSys'10, Barcelona, Spain, September 26-30, pp. 221-224. ACM, NewYork, NY, USA.
25. Özbal, G., Karaman, H. and Alpaslan, F.N. (2011) A content-boosted collaborative filtering approach for movie recommendation based on local and global similarity and missing data prediction. Comput. J., 54, 1535-1546.
26. Guan, Z.,Wang, C., Bu, J., Chen, C.,Yang, K., Cai, D. and He, X. (2010) Document Recommendation in Social Tagging Services. Proc. WWW 10, Raleigh, North Carolina, USA, April 26-30, pp. 391-400. ACM, NewYork, NY, USA.
27. Bogers, T. and Van Den Bosch, A. (2011) Fusing recommendations for social bookmarking web sites. Int. J. Electron. Commer., 15, 31-72.
28. Tavakolifard, M. andAlmeroth, K.C. (2012) Social computing: an intersection of recommender systems, trust/reputation systems, and social networks. IEEE Netw., 26, 53-58.
29. Pudota, N., Dattolo, A., Baruzzo, A., Ferrara, F. and Tasso, C. (2010) Automatic keyphrase extraction and ontology mining for content-based tag recommendation. Int. J. Intell. Syst., 25, 1158-1186.
30. Hsu, M.H. and Chen H.H. (2011) Efficient and effective prediction of social tags to enhance web search. J. Am. Soc. Inf. Sci. Technol., 62, 1473-1487.
31. Figueiredo, F., Pinto, H., Belúm, F., Almeida, J., Gonçalves, M., Fernandes, D. and Moura, E. (2013) Assessing the quality of textual features in social media. Inf. Process. Manage., 49, 222-247.
32. Manning, C.D., Raghavan, P. and Schütze, H. (2008) Introduction to Information Retrieval. Cambridge University Press, Cambridge.
33. Quattrone, G., Capra, L., De Meo, P., Ferrara, E. and Ursino, D. (2011) Effective Retrieval of Resources in Folksonomies using a New Tag Similarity Measure. Proc. CIKM'11, Glasgow, Scotland, UK, October 24-28, pp. 545-550. ACM, New York, NY, USA.
34. Liben-Nowell, D. and Kleinberg, J. (2007) The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Tecechnol., 58, 1019-1031.
35. Symeonidis, P., Tiakas, E. and Manolopoulos, Y. (2011) Product Recommendation and Rating Prediction Based on Multi-Modal Social Networks. Proc. RecSys 11, Chicago, IL, USA, October 23-27, pp. 61-68. ACM, NewYork, NY, USA.
36. Bigot, A., Chrisment, C., Dkaki, T., Hubert, G. and Mothe, J. (2011) Fusing different information retrieval systems according to query-topics: a study based on correlation in information retrieval systems and TREC topics. Inf. Retr., 14, 617-648.
37. Macdonald, C. and Ounis, I. (2009) Searching for expertise: experiments with the voting model. Comput. J., 52, 729-748.
38. Wu, S. (2012) Applying the data fusion technique to blog opinion retrieval. Expert Syst. Appl., 39, 1346-1353.
39. Fox, E. and Shaw, J. (1994) Combination of Multiple Searches. Proc. TREC-2, Gaithersburg, MD, USA, pp. 243-252. NIST.
40. Aslam, J.A. and Montague, M. (2001) Models for Metasearch. Proc. SIGIR 01, New Orleans, LA, USA, September 9-13, pp. 276-284. ACM, NewYork, NY, USA.
41. Montague, M. and Aslam, J.A. (2002) Condorcet Fusion for Improved Retrieval. Proc. CIKM 02, McLean, VA, USA, November 4-9, pp. 538-548. ACM, New York, NY, USA.
42. Lillis, D., Zhang, L., Toolan, F., Collier, R.W., Leonard, D. and Dunnion, J. (2010) Estimating Probabilities for Effective Data Fusion. Proc. SIGIR'10, Geneva, Switzerland, July 19-23, pp. 347-354. ACM, NewYork, NY, USA.
43. Croft,W. B., Metzler, D. and Strohman,T. (2010) Search Engines: Information Retrieval in Practice. Addison-Wesley Publishing Company, USA.
44. Nandakumar, K., Chen, Y., Dass, S.C. and Jain, A.K. (2008) Likelihood ratio-based biometric score fusion. IEEE Trans. Pattern Anal., 30, 342-347.