A Hybrid Method Based on HITS for Literature Recommendation

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In this paper we propose a hybrid method of literature recommendation in the academic community. First, we refer the objective recommendation based on HITS algorithm by constructing a directed graph according to the literature citation relation and then select the articles considering the authority and hub score of each article synthetically and add them to the recommendation list. This can narrow the recommendation scope and give a more authoritive recommendation. Second, the subjective recommendation is based on collaborative filtering by comparing the ratings of other similar users for the objects in recommendation list. The difference is we discover the similar user by clustering them. And the experiment shows the method can provide better recommendation results and is timesaving.

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Periodical:

Edited by:

Qi Luo

Pages:

1636-1641

Citation:

P. Y. Zhang et al., "A Hybrid Method Based on HITS for Literature Recommendation", Applied Mechanics and Materials, Vols. 55-57, pp. 1636-1641, 2011

Online since:

May 2011

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$41.00

[1] S. -Y. Hwang et al., Coauthorship networks and academic literature recommendation Electronic Commerce Research and Applications 9 (2010) 323–334.

DOI: https://doi.org/10.1016/j.elerap.2010.01.001

[2] Pennock, D., Horvitz, E., Lawrence, S. and Giles, C. (2000), Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach, Proceedings of the Conference on Uncertainty in Artificial Intelligence, pp.473-80.

[3] Hwang, S-Y., Hsiung, W-C. and Yang, W-S. (2003), A prototype WWW literature recommendation system for digital libraries, Online Information Review, Vol. 27 No. 3, pp.169-82.

DOI: https://doi.org/10.1108/14684520310481436

[4] Hwang, S. Y., and Chuang, S. M. Combining article content and web usage for literature recommendation in digital libraries. Online Information Review, 28, 4, 2004, 260–272.

DOI: https://doi.org/10.1108/14684520410553750

[5] Chen Zuqin, Zhang Huiling et al. Related Document Recommending Based on Weighted Association Rule Mining. Modern library information technique. (2007).

[6] Li Xueling, Zhai Xuemei. The discussion of citation analysis based on PageRank. Information System. (2007).

[7] W. Bruce Croft, Donald metzler, Search Engines Information Retrieval in Practice, China Machine Press, Beijing, (2009).

[8] P.Y. Zhang Y.J. Du and C. Wang , Clustering users according to Common Interest Based on User Search Behavior, Advanced Materials Research Journals2010(in press).

[9] Kai Li, Yajun Dun, Dan Xiang, Collaborative Recommending based on Core-Concept Lattice, in: IFSA 2007 World Congress, Cancun, 2007, pp.583-592.

DOI: https://doi.org/10.1007/978-3-540-72434-6_59

[10] Jiyi WU, et al., Intelligent collaborative filtering-based personalized recommender systems in Mobile E-commerce, Journal of Computational Information System5: 3(2009) 1623-1630.

[11] http: /en. wikipedia. org/wiki/CiteSeerX.