Abstract
Evaluating the similarity between two objects in heterogeneous information network is a significant part of information science. The existing meta-structure based similarity measures only consider one meta-structure, which leads to a loss of accuracy. Based on the meta-structure, this paper proposes a weighted method to tackle the problem. We put forward a weighting algorithm that determines the value of weight to each meta-structure according to the set of the user’s preferences, and to compute the similarity value, we convert meta-structure into meta-path and use a novel meta-path based similarity measure StruSim. The top-k similarity research experiment is conducted to prove the effectiveness of the novel method. Using the measure nDCG, we conclude that StruSim performs better than PathSim, HeteSim, and AvgSim. And the multiple meta-structure methods are better than BSCSE and unweighted meta-path based methods. At last, we propose an interpolation and derivation method to search the optimal bias factor in StruSim to achieve a better performance.
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References
Fouss, F., Pirotte, A., Renders, J.M., Saerens, M.: Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans. Knowl. Data Eng. 19(3), 355–369 (2007)
Huang, Z., Zheng, Y., Cheng, R., Sun, Y., Mamoulis, N., Li, X.: Meta structure: computing relevance in large heterogeneous information networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1595–1604. ACM (2016)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. (TOIS) 20(4), 422–446 (2002)
Lao, N., Cohen, W.W.: Fast query execution for retrieval models based on path-constrained random walks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 881–888. ACM (2010)
Lao, N., Cohen, W.W.: Relational retrieval using a combination of path-constrained random walks. Mach. Learn. 81(1), 53–67 (2010)
Ley, M.: DBLP computer science bibliography (2005)
Meng, C., Cheng, R., Maniu, S., Senellart, P., Zhang, W.: Discovering meta-paths in large heterogeneous information networks. In: Proceedings of the 24th International Conference on World Wide Web, pp. 754–764. International World Wide Web Conferences Steering Committee (2015)
Meng, X., Shi, C., Li, Y., Zhang, L., Wu, B.: Relevance measure in large-scale heterogeneous networks. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds.) APWeb 2014. LNCS, vol. 8709, pp. 636–643. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11116-2_61
Shi, C., Kong, X., Huang, Y., Yu, P.S., Wu, B.: HeteSim: a general framework for relevance measure in heterogeneous networks. IEEE Trans. Knowl. Data Eng. 26(10), 2479–2492 (2014)
Shi, C., Yu, P.S.: Heterogeneous Information Network Analysis and Applications. Data Analytics. Springer, New York (2017). https://doi.org/10.1007/978-3-319-56212-4
Shi, C., Zhang, Z., Luo, P., Yu, P.S., Yue, Y., Wu, B.: Semantic path based personalized recommendation on weighted heterogeneous information networks. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM 2015, pp. 453–462. ACM (2015)
Sun, Y., Han, J.: Meta-path-based search and mining in heterogeneous information networks. Tsinghua Sci. Technol. 18(4), 329–338 (2013)
Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: PathSim: meta path-based top-k similarity search in heterogeneous information networks. PVLDB 4(11), 992–1003 (2011)
Sun, Y., Norick, B., Han, J., Yan, X., Yu, P.S., Yu, X.: Integrating meta-path selection with user-guided object clustering in heterogeneous information networks. In: Yang, Q., Agarwal, D., Pei, J. (eds.) The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012, Beijing, China, 12–16 August 2012, pp. 1348–1356. ACM (2012)
Tang, Z.P., Yang, Y., Bu, Y.: Weighted-pathSim: similarity measure for plot-based movie recommendation
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Li, Z., Wang, H. (2018). A Weighted Similarity Measure Based on Meta Structure in Heterogeneous Information Networks. In: Yoshida, K., Lee, M. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2018. Lecture Notes in Computer Science(), vol 11016. Springer, Cham. https://doi.org/10.1007/978-3-319-97289-3_22
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DOI: https://doi.org/10.1007/978-3-319-97289-3_22
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