Abstract
Robo-advisor is a type of financial recommendation that can provide investors with financial advice or investment management online. Data clustering and item recommendation are both important and challenging in Robo-advisor. These two tasks are often considered independently and most efforts have been made to tackle them separately. However, users in data clustering and group relationship in item recommendation are inherently related. For example, a large number of financial transactions include not only the user’s asset information, but also the user’s social information. The existence of relations between users and groups motivates us to jointly perform clustering and item recommendation for Robo-advisor in this paper. In particular, we provide a principle way to capture the relations between users and groups, and propose a novel framework CLURE, which fuses data CLUstering and item REcommendation into a coherent model. With experiments on benchmark and real-world datasets, we demonstrate that the proposed framework CLURE achieves superior performance on both tasks compared to the state-of-the-art methods.
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References
Goeke M.: Kompetenz und Trends im Private Banking. Banking and Innovation 2016. Springer Fachmedien Wiesbaden, 3–9(2016)
Xue, J., Liu, Q., Li, M., et al.: Incremental multiple kernel extreme learning machine and its application in Robo-advisors. Soft Computing 4, 1–11 (2018)
Xue J, Huang L, Liu Q.: A Bi-directional Evolution Algorithm for Financial Recommendation Model. Theoretical Computer Science. 341–354(2017)
Jung, D., Dorner, V., Weinhardt, C., et al.: Designing a robo-advisor for risk-averse, low-budget consumers. Electron Markets, 2017
Jung, D., Dorner, V., Glaser, F., et al.: Robo-Advisory: Digitalization and Automation of Financial Advisory. Business and Information Systems Engineering 60(1), 81–86 (2018)
Zhang, J., Lin, Y., Lin, M., et al.: An effective collaborative filtering algorithm based on user preference clustering. Applied Intelligence 45(2), 230–240 (2016)
Wang Y, Wang S, Tang J, et al.: CLARE: A Joint Approach to Label Classification and Tag Recommendation. AAAI. 2017
Huang, Z., Chung, W., Chen, H.: A graph model for E-commerce recommender systems. Journal of the American Society for Information Science & Technology 55(3), 259–274 (2014)
Felfernig A, Zachar P, Zachar P, et al.: The VITA financial services sales support environment. National Conference on Innovative Applications of Artificial Intelligence. AAAI Press, 1692–1699 (2007)
Fr Sayyed, Rv Argiddi, Ss Apte.: GENERATING RECOMMENDATIONS FOR STOCK MARKET USING COLLABORATIVE FILTERING. International Journal of Computer Engineering & Science, 46–49 (2013)
Parikh V, Shah P.: E-commerce Recommendation System using Association Rule Mining and Clustering. International Journal of Innovations and Advancement in Computer Science. 2015
Paranjape-Voditel P, Deshpande U.: An Association Rule Mining Based Stock Market Recommender System. International Conference on Emerging Applications of Information Technology. IEEE, 21–24 (2011)
Yang, Yujun and Li, Jianping and Yang, Yimei.: An Efficient Stock Recommendation Model Based on Big Order Net Inflow. Mathematical Problems in Engineering. (9):1–15 (2016)
Lili Zhao, Zhongqi Lu, Sinno Jialin Pan, Qiang Yang.: Matrix Factorization+ for Movie Recommendation. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 3945–3951 (2016)
Cheng, H., Lu, Y.C., Sheu, C.: An ontology-based business intelligence application in a financial knowledge management system. Expert Systems with Applications 36(2), 3614–3622 (2009)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Advances in Artificial Intelligence 2009(12), 4 (2009)
Hofmann, T.: Latent semantic models for collaborative filtering. ACM Transactions on Information Systems 22(1), 89–115 (2017)
Huang, Z., Chen, H., Zeng, D.: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems 22(1), 116–142 (2015)
Gomez-Uribe, C.A., Hunt, N.: The Netflix Recommender System. ACM Transactions on Management Information Systems 6(4), 1–19 (2015)
Wang, S., Huang, S., Liu, T.Y., et al.: Ranking-Oriented Collaborative Filtering: A List wise Approach. ACM Transactions on Information Systems 35(2), 10 (2016)
Yang, L., Hsieh, C.K., Yang, H., et al.: Yum-Me: A Personalized Nutrient-Based Meal Recommender System. ACM Transactions on Information Systems 36(1), 7 (2017)
Liu X, Zhou S, Wang Y, et al.: Optimal Neighborhood Kernel Clustering with Multiple Kernels. AAAI. 2017
H Zhao, Z Ding, Y Fu, et al.: Multi-View Clustering via Deep Matrix Factorization. AAAI. 2017
Yang, D., Yang, D., Yang, D., et al.: Learning Informative Priors from Heterogeneous Domains to Improve Recommendation in Cold-Start User Domains. ACM Transactions on Information Systems 35(2), 13 (2016)
Kelly, J.L.: A New Interpretation of Information Rate. Ire Transactions on Information Theory 2(3), 185–189 (2003)
Hu, W., Yang, F., Feng, Z.: Item-based collaborative filtering recommendation algorithm based on MapReduce[M]. Multimedia, Communication and Computing Application (2015)
Zhang J, Lin Z, Xiao B, et al.: An optimized item-based collaborative filtering recommendation algorithm. IEEE International Conference on Network Infrastructure and Digital Content, Ic-Nidc. IEEE, 414–418 (2009)
Pazzani M J, Billsus D.: Content-Based Recommendation Systems. Adaptive Web. Springer-Verlag, 325–341 (2007)
Liu Y, Tong Q, Du Z, et al.: Content-Boosted Restricted Boltzmann Machine for Recommendation. In: Wermter S. et al. (eds) Artificial Neural Networks and Machine Learning, 8681:773–780 (2014)
Burke, Robin.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, 12(4):331–370 (2002)
Zibriczky D.: Recommender Systems meet Finance: A literature review. International Workshop on Personalization and Recommender Systems in Financial Services. 2016
Goldberg, D., Nichols, D., Oki, B.M., et al.: Using collaborative filtering to weave an information tapestry. Communications of the Acm 35(12), 61–70 (1992)
Bobadilla, J., Hernando, A., Ortega, F., et al.: Collaborative filtering based on significances. Information Sciences An International Journal 185(1), 1–17 (2012)
Leavitt, N.: A Technology that Comes Highly Recommended. Computer 46(3), 14–17 (2013)
S Sedhain, AK Menon, S Sanner, et al.: Low-Rank Linear Cold-Start Recommendation from Social Data. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence(AAAI2017), 2017
C Sha, X Wu, J Niu.: A Framework for Recommending Relevant and Diverse Items. Proceedings of the Twenty-International Joint Conference on Artificial Intelligence (IJCAI-16), 2016
Son J W, Jeon J, Lee A, et al.: spectral clustering with brainstorming process for multi-view data. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI2017), 2017
Liu X, Zhou S, Wang Y, et al.: Optimal Neighborhood Kernel Clustering with Multiple Kernels. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI2017), 2017
Kalintha W, Ono S, Numao M, et al.: Kernelized Evolutionary Distance Metric Learning for Semi-supervised Clustering. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence(AAAI2017), 2017
Roweis, S.T., Saul, L.K.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290(5500), 2323–6 (2000)
Gong, S.: A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering. Journal of Software 5(7), 745–752 (2010)
Sobhanam H, Mariappan A K.: Addressing cold start problem in recommender systems using association rules and clustering technique. International Conference on Computer Communication and Informatics. IEEE, 1–5 (2013)
Chen, K., Peng, Z., Ke, W.: Study on collaborative filtering recommendation algorithm based on web user clustering. International Journal of Wireless and Mobile Computing 5(4), 401–408 (2012)
Chen, K.H.: User Clustering Based Social Network Recommendation. Chinese Journal of Computers 36(2), 349–359 (2013)
Bobadilla, J., Hernando, A., Ortega, F., et al.: A framework for collaborative filtering recommender systems. Expert Systems with Applications An International Journal 38(12), 14609–14623 (2011)
Harper F M, Konstan J A.: The MovieLens Datasets: History and Context. ACM, 2016
Acknowledgments
This work was supported by the National Key R&D Program of China 2018YFB1003203 and the Natural Science Foundation of China (Grant No. 61672528, 61773392, 61702539).
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Xue, J., Zhu, E., Liu, Q., Wang, C., Yin, J. (2018). A Joint Approach to Data Clustering and Robo-Advisor. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11063. Springer, Cham. https://doi.org/10.1007/978-3-030-00006-6_9
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DOI: https://doi.org/10.1007/978-3-030-00006-6_9
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