Abstract
In recent years, as more and more big data platforms have been applied to the government network systems, it’s essential to adopt an effective query-based recommendation algorithm to help officers find out the needed table with a keyword. The key challenge lies in the complex relationship among data from different departments, which cannot be easily solved by the existing database query methods. The Heterogeneous Information Network (HIN) is a specific type of networks developed for modeling complex data relations. However, these existing query-based recommendation algorithms could not make use of HIN. Besides, many query-based recommendation algorithms could not make recommendations with the keyword that is not in the query records. In addition, most of the existing recommendation algorithms do not make full use of the semantic meanings. Therefore, in this paper, by making use of the real dataset provided by the local government, the proposed method is the first to use the pretrained word embeddings and LSTM (PRE-LSTM) to train the network embeddings and to learn the relationship among the tables, departments and the keywords, so that it can make use of the data in HIN and enable the network embeddings to obtain the most precise semantic meaning. Additionally, our algorithm uses word embeddings to represent query keywords so as to let our algorithm make the query-based recommendation for nearly any query. Using the trained embeddings and the PRE-LSTM model, the proposed algorithm is able to show user-specific recommendation results sorted in a reasonable order. Experimental results on the real data application tasks confirm the effectiveness of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Allen, C., Hospedales, T.M.: Analogies explained: towards understanding word embeddings. In: ICML 2019, pp. 223–231 (2019)
Arora, S., Li, Y., Liang, Y., Ma, T., Risteski, A.: A latent variable model approach to pmi-based word embeddings. TACL 4, 385–399 (2016)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. 30(1–7), 107–117 (1998)
Cai, X., Han, J., Pan, S., Yang, L.: Heterogeneous information network embedding based personalized query-focused astronomy reference paper recommendation. Int. J. Comput. Intell. Syst. 11(1), 591–599 (2018)
Chen, Y., Wang, T., Chen, W., Li, Q., Qiu, Z.: Type sequence preserving heterogeneous information network embedding. In: AAAI 2019, pp. 9931–9932 (2019)
He, Y., Song, Y., Li, J., Ji, C., Peng, J., Peng, H.: HeteSpaceyWalk: a heterogeneous spacey random walk for heterogeneous information network embedding. In: CIKM 2019, pp. 639–648 (2019)
Huang, Z., Cautis, B., Cheng, R., Zheng, Y., Mamoulis, N., Yan, J.: Entity-based query recommendation for long-tail queries. TKDD 12(6), 64:1–64:24 (2018)
Jiang, H., Song, Y., Wang, C., Zhang, M., Sun, Y.: Semi-supervised learning over heterogeneous information networks by ensemble of meta-graph guided random walks. In: IJCAI 2017, pp. 1944–1950 (2017)
Lu, Y., Shi, C., Hu, L., Liu, Z.: Relation structure-aware heterogeneous information network embedding. In: AAAI 2019, pp. 4456–4463 (2019)
Ma, C., Zhang, B.: A new query recommendation method supporting exploratory search based on search goal shift graphs. IEEE Trans. Knowl. Data Eng. 30(11), 2024–2036 (2018)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS 2013, pp. 3111–3119 (2013)
Mikolov, T., Yih, W., Zweig, G.: Linguistic regularities in continuous space word representations. In: NAACL 2013, pp. 746–751 (2013)
Shi, C., Hu, B., Zhao, W.X., Yu, P.S.: Heterogeneous information network embedding for recommendation. IEEE Trans. Knowl. Data Eng. 31(2), 357–370 (2019)
Sun, Y., Han, J.: Mining heterogeneous information networks: a structural analysis approach. SIGKDD Explor. 14(2), 20–28 (2012)
Sun, Y., Han, J.: Mining Heterogeneous Information Networks: Principles and Methodologies. Synthesis Lectures on Data Mining and Knowledge Discovery. Morgan & Claypool Publishers, San Rafael (2012)
Sun, Y., Yu, Y., Han, J.: Ranking-based clustering of heterogeneous information networks with star network schema. In: KDD 2009, pp. 797–806 (2009)
Szpektor, I., Gionis, A., Maarek, Y.: Improving recommendation for long-tail queries via templates. In: WWW 2011, pp. 47–56 (2011)
Wang, C.: Meta-path constrained random walk inference for large-scale heterogeneous information networks. CoRR abs/1912.00634 (2019)
Wang, H., Zhang, F., Hou, M., Xie, X., Guo, M., Liu, Q.: SHINE: signed heterogeneous information network embedding for sentiment link prediction. In: WSDM 2018, pp. 592–600 (2018)
Wang, X., Zhang, Y., Shi, C.: Hyperbolic heterogeneous information network embedding. In: AAAI 2019, pp. 5337–5344 (2019)
Xiang, E.W., Liu, N.N., Pan, S.J., Yang, Q.: Knowledge transfer among heterogeneous information networks. In: ICDM 2009, pp. 429–434 (2009)
Xu, J., Ye, F., Yu, H., Wang, B.: Query recommendation based on improved query flow graph\(^{*}\). In: IJCNN 2019, pp. 1–8 (2019)
Yang, L., Zheng, Y., Cai, X., Pan, S., Dai, T.: Query-oriented citation recommendation based on network correlation. J. Intell. Fuzzy Syst. 35(4), 4621–4628 (2018)
Yuan, W., He, K., Han, G., Guan, D., Khattak, A.M.: User behavior prediction via heterogeneous information preserving network embedding. Future Gener. Comp. Syst. 92, 52–58 (2019)
Zhu, Y., et al.: Query-based interactive recommendation by meta-path and adapted attention-GRU. In: CIKM 2019, pp. 2585–2593 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, ZM., Hui, YM., Huang, L. (2020). Query-Based Recommendation by HIN Embedding with PRE-LSTM. In: Yang, X., Wang, CD., Islam, M.S., Zhang, Z. (eds) Advanced Data Mining and Applications. ADMA 2020. Lecture Notes in Computer Science(), vol 12447. Springer, Cham. https://doi.org/10.1007/978-3-030-65390-3_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-65390-3_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65389-7
Online ISBN: 978-3-030-65390-3
eBook Packages: Computer ScienceComputer Science (R0)