Abstract
Under the background of the Judicial Reform of China, big data of judicial cases are widely used to solve the problem of judicial research. Similarity analysis of judicial cases is the basis of wisdom judicature. In view of the necessity of getting rid of the ineffective information and extracting useful rules and conditions from the descriptive document, the analysis of Chinese judicial cases with a certain format is a big challenge. Hence, we propose a method that focuses on producing recommendations that are based on the content of judicial cases. Considering the particularity of Chinese language, we use “jieba” text segmentation to preprocess the cases. In view of the lack of labels of user interest and behavior, the proposed method considers the content information via adopting TF-IDF combined with LDA topic model, as opposed to the traditional methods such as CF (Collaborative Filtering Recommendations). Users are recommended to compute cosine similarity of cases in the same topic. In the experiments, we evaluate the performance of the proposed model on a given dataset of nearly 200,000 judicial cases. The experimental result reveals when the number of topics is around 80, the proposed method gets the best performance.
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References
Becker, G.S., Landes, W.M.: Essays in the Economics of Crime and Punishment. Number 3 in Human Behavior and Social Institutions. National Bureau of Economic Research: Distributed by Columbia University Press
He, T., Lian, H., Qin, Z., Zou, Z., Luo, B.: Word embedding based document similarity for the inferring of penalty. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds.) WISA 2018. LNCS, vol. 11242, pp. 240–251. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02934-0_22
He, T.-K., Lian, H., Qin, Z.-M., Chen, Z.-Y., Luo, B.: PTM: a topic model for the inferring of the penalty. J. Comput. Sci. Technol. 33(4), 756–767 (2018)
Qin, Z., He, T., Lian, H., Tian, Y., Liu, J.: Research on judicial data standard. In: 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), pp. 175–177. IEEE (2018)
Balabanovic, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40, 66–72 (1997)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering
Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: Proceedings of the 16th International Conference on World Wide Web - WWW 2007, p. 271. ACM Press (2007)
Badaro, G., Hajj, H., El-Hajj, W., Nachman, L.: A hybrid approach with collaborative filtering for recommender systems. In: 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 349–354, July 2013
Strub, F., Mary, J., Gaudel, R.: Hybrid collaborative filtering with autoencoders (2016)
Ahn, H.J.: A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf. Sci. 178(1), 37–51 (2008)
Patra, B.Kr., Launonen, R., Ollikainen, V., Nandi, S.: A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data. Knowl.-Based Syst. 82(C), 163–177 (2015)
Ekstrand, M.D.: Collaborative filtering recommender systems 4(2), 81–173
Lin, W., Alvarez, S.A., Ruiz, C.: Efficient adaptive-support association rule mining for recommender systems. Data Min. Knowl. Disc. 6(1), 83–105 (2002)
Kardan, A.A., Ebrahimi, M.: A novel approach to hybrid recommendation systems based on association rules mining for content recommendation in asynchronous discussion groups. Inf. Sci. 219, 93–110 (2013)
Nagori, R., Aghila, G.: LDA based integrated document recommendation model for e-learning systems, pp. 230–233, April 2011
Luostarinen, T., Kohonen, O.: Using topic models in content-based news recommender systems. In: Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013), pp. 239–251. Linköping University Electronic Press, Sweden (2013)
Pennacchiotti, M., Gurumurthy, S.: Investigating topic models for social media user recommendation. In: Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011, pp. 101–102. ACM, New York (2011)
Ramos, J.: Using TF-IDF to determine word relevance in document queries
Blei, D.M.: Latent Dirichlet allocation, p. 30
Arora, K.: Contrastive perplexity: a new evaluation metric for sentence level language models. CoRR, abs/1601.00248 (2016)
Yin, H., Sun, Y., Cui, B., Hu, Z., Chen, L.: LCARS: a location-content-aware recommender system, pp. 221–229, August 2013
Acknowledgment
The work is supported in part by the National Key Research and Development Program of China (2016YFC0800805) and the National Natural Science Foundation of China (61772014).
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Guo, Z., He, T., Qin, Z., Xie, Z., Liu, J. (2019). A Content-Based Recommendation Framework for Judicial Cases. In: Cheng, X., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1058. Springer, Singapore. https://doi.org/10.1007/978-981-15-0118-0_7
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DOI: https://doi.org/10.1007/978-981-15-0118-0_7
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