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A multi-attribute decision making approach based on information extraction for real estate buyer profiling

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Abstract

With the rapid development of the Internet and the widespread usage of mobile terminals, data-driven user profiling has become possible. User profiles describe the user’s overall behavior characteristic from multiple perspectives (e.g. basic information, feature preference, social attribute), which can explore the potential relationships between complex user behaviors and the decision-making process. In this paper, we focus on the problem of real estate buyer profiling and propose a novel multi-attribute decision making (MADM) approach, trying to solve the needs of enterprises to locate target customers accurately. Firstly, we reorganize the dataset by integrating structured with unstructured data, where an Enriched Bi-directional long short-term memory (Bi-LSTM) Conditional Random Field (EB-CRF) model is proposed to extract important information in the unstructured data. Based on four general dimensions (i.e. basic information, family situation, purchase intention, financial situation), we then design an entropy-based weight allocation algorithm to obtain attribute weights, which helps explore implicit heterogeneous relationships. Finally, with the help of expert knowledge, we use attribute weights and representation technology “bag of attributes” to construct a buyer-specific feature representation. Extensive experimental results indicate that our approach outperforms strong baselines significantly and achieves state-of-the-art performance.

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References

  1. Alzaidy, R., Caragea, C., Giles, C.L.: Bi-lstm-crf sequence labeling for keyphrase extraction from scholarly documents. In: Liu L., White R.W., Mantrach A., Silvestri F., McAuley J.J., Baeza-Yates R., Zia L. (eds.) The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019, pp. 2551–2557. ACM (2019). https://doi.org/10.1145/3308558.3313642

  2. CAI, T., Li, J., Mian, A.S., li, R., Sellis, T., Yu, J.X.: Target-aware holistic influence maximization in spatial social networks. IEEE Transactions on Knowledge and Data Engineering, 1–1 (2020). https://doi.org/10.1109/TKDE.2020.3003047

  3. Chen, W., Chan, H.P., Li, P., King, I.: Exclusive hierarchical decoding for deep keyphrase generation. In: Jurafsky D., Chai J., Schluter N., Tetreault J.R. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pp. 1095–1105. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.acl-main.103

  4. Chen, J., Zhang, X., Wu, Y., Yan, Z., Li, Z.: Keyphrase generation with correlation constraints. In: Riloff E., Chiang D., Hockenmaier J., Tsujii J. (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, pp. 4057–4066. Association for Computational Linguistics (2018). https://doi.org/10.18653/v1/d18-1439

  5. Chen, J., Zhong, M., Li, J., Wang, D., Qian, T., Tu, H.: Effective deep attributed network representation learning with topology adapted smoothing. IEEE Transactions on Cybernetics, 1–12 (2021). https://doi.org/10.1109/TCYB.2021.3064092

  6. Chen, T., Li, C.: Determining objective weights with intuitionistic fuzzy entropy measures: A comparative analysis. Inf. Sci. 180(21), 4207–4222 (2010). https://doi.org/10.1016/j.ins.2010.07.009

    Article  Google Scholar 

  7. Chen, T., Li, C.: Objective weights with intuitionistic fuzzy entropy measures and computational experiment analysis. Appl. Soft Comput. 11(8), 5411–5423 (2011). https://doi.org/10.1016/j.asoc.2011.05.018

    Article  Google Scholar 

  8. Chin, K., Fu, C., Wang, Y.: A method of determining attribute weights in evidential reasoning approach based on incompatibility among attributes. Comput. Ind. Eng. 87, 150–162 (2015). https://doi.org/10.1016/j.cie.2015.04.016

    Article  Google Scholar 

  9. Constantinides, M., Dowell, J.: A framework for interaction-driven user modeling of mobile news reading behaviour. In: Mitrovic T., Zhang J., Chen L., Chin D. (eds.) Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, UMAP 2018, Singapore, July 08-11, 2018, pp. 33–41 (2018). https://doi.org/10.1145/3209219.3209229

  10. Deng, H., Yeh, C., Willis, R.J.: Inter-company comparison using modified TOPSIS with objective weights. Comput. Oper. Res. 27(10), 963–973 (2000). https://doi.org/10.1016/S0305-0548(99)00069-6

    Article  MATH  Google Scholar 

  11. Deng, M., Xu, W., Yang, J.: Estimating the attribute weights through evidential reasoning and mathematical programming. Int. J. Inf. Technol. Decis. Mak. 3(3), 419–428 (2004). https://doi.org/10.1142/S0219622004001124

    Article  Google Scholar 

  12. Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein J., Doran C., Solorio T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/n19-1423

  13. Diao, M., Zhang, Z., Su, S., Gao, S., Cao, H.: UPON: user profile transferring across networks. In: d’Aquin M., Dietze S., Hauff C., Curry E., Cudré-Mauroux P. (eds.) CIKM ’20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020, pp. 265–274 (2020). https://doi.org/10.1145/3340531.3411964

  14. Du, J., Michalska, S., Subramani, S., Wang, H., Zhang, Y.: Neural attention with character embeddings for hay fever detection from twitter. Health Inf. Sci. Syst. 7(1), 21 (2019). https://doi.org/10.1007/s13755-019-0084-2

    Article  Google Scholar 

  15. Fan, Z., Ma, J., Zhang, Q.: An approach to multiple attribute decision making based on fuzzy preference information on alternatives. Fuzzy Sets Syst. 131(1), 101–106 (2002). https://doi.org/10.1016/S0165-0114(01)00258-5

    Article  MATH  Google Scholar 

  16. Gu, H., Wang, J., Wang, Z., Zhuang, B., Su, F.: Modeling of user portrait through social media. In: 2018 IEEEx International Conference on Multimedia and Expo, ICME 2018, San Diego, CA, USA, July 23-27, 2018, pp. 1–6 (2018). https://doi.org/10.1109/ICME.2018.8486595

  17. Han, Y., Zhang, H., Zhao, Y.: Structural evolution of real estate industry in china: 2002–2017. Structural Change and Economic Dynamics 57, 45–56 (2021). https://doi.org/10.1016/j.strueco.2021.01.010

    Article  Google Scholar 

  18. Hasan, K.S., Ng, V.: Automatic keyphrase extraction: A survey of the state of the art. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014, June 22-27, 2014, Baltimore, MD, USA, Volume 1: Long Papers, pp. 1262–1273. The Association for Computer Linguistics (2014). https://doi.org/10.3115/v1/p14-1119

  19. Horowitz, I., Zappe, C.: The linear programming alternative to policy capturing for eliciting criteria weights in the performance appraisal process. Omega 23(6), 667–676 (1995). https://doi.org/10.1016/0305-0483(95)00039-9

    Article  Google Scholar 

  20. Hou, M., Ren, J., Zhang, D., Kong, X., Zhang, D., Xia, F.: Network embedding: Taxonomies, frameworks and applications. Computer Science Review 38, 100,296 (2020). https://doi.org/10.1016/j.cosrev.2020.100296

  21. Jiao, Z., Sun, S., Sun, K.: Chinese lexical analysis with deep bi-gru-crf network. arXiv:1807.01882 (2018)

  22. Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. In: Lapata, M., Blunsom, P., Koller A. (eds.) Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017, Valencia, Spain, April 3-7, 2017, Volume 2: Short Papers, pp. 427–431. Association for Computational Linguistics (2017). https://doi.org/10.18653/v1/e17-2068

  23. Kong, X., Zhang, J., Zhang, D., Bu, Y., Xia, F.: The gene of scientific success. ACM Trans. Knowl. Discov. Data 14(4), 41:1-41:19 (2020). https://doi.org/10.1145/3385530

    Article  Google Scholar 

  24. Kong, X., Li, J., Wang, L., Shen, G., Sun, Y., Lee, I.: Recurrent-dc: A deep representation clustering model for university profiling based on academic graph. Future Generation Computer Systems 116, 156–167 (2021). https://doi.org/10.1016/j.future.2020.10.019

    Article  Google Scholar 

  25. Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Brodley C.E., Danyluk A.P. (eds.) Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pp. 282–289. Morgan Kaufmann (2001)

  26. Li, J., Cai, T., Deng, K., Wang, X., Sellis, T., Xia, F.: Community-diversified influence maximization in social networks. Information Systems 92, 101,522 (2020). https://doi.org/10.1016/j.is.2020.101522

    Article  Google Scholar 

  27. Li, Z., Wang, X., Li, J., Zhang, Q.: Deep attributed network representation learning of complex coupling and interaction. Knowl. Based Syst. 212, 106,618 (2021). https://doi.org/10.1016/j.knosys.2020.106618

    Article  Google Scholar 

  28. Ma, J., Fan, Z., Huang, L.: A subjective and objective integrated approach to determine attribute weights. Eur. J. Oper. Res. 112(2), 397–404 (1999). https://doi.org/10.1016/S0377-2217(98)00141-6

    Article  MATH  Google Scholar 

  29. Mezghani, M., Zayani, C.A., Amous, I., Gargouri, F.: A user profile modelling using social annotations: a survey. In: Mille A, Gandon F, Misselis J, Rabinovich M, Staab S. (eds.) Proceedings of the 21st World Wide Web Conference, WWW 2012, Lyon, France, April 16-20, 2012 (Companion Volume), pp. 969–976 (2012). https://doi.org/10.1145/2187980.2188230

  30. Mi, X., Tian, Y., Kang, B.: A hybrid multi-criteria decision making approach for assessing health-care waste management technologies based on soft likelihood function and d-numbers. Appl. Intell. 51(10), 6708–6727 (2021). https://doi.org/10.1007/s10489-020-02148-7

    Article  Google Scholar 

  31. Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Moschitti A., Pang B., Daelemans W. (eds.) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1532–1543 (2014). https://doi.org/10.3115/v1/d14-1162

  32. Song, X., Li, J., Tang, Y., Zhao, T., Chen, Y., Guan, Z.: Jkt: A joint graph convolutional network based deep knowledge tracing. Information Sciences 580, 510–523 (2021). https://doi.org/10.1016/j.ins.2021.08.100

    Article  Google Scholar 

  33. Sun, Y., Chai, R.: An early-warning model for online learners based on user portrait. Ingénierie des Systèmes d Inf. 25(4), 535–541 (2020). https://doi.org/10.18280/isi.250418

  34. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Guyon I., von Luxburg U., Bengio S., Wallach H.M., Fergus R., Vishwanathan S.V.N., Garnett R. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pp. 5998–6008 (2017). https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html

  35. Wang, Y., Luo, Y.: Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Math. Comput. Model. 51(1–2), 1–12 (2010). https://doi.org/10.1016/j.mcm.2009.07.016

    Article  MATH  Google Scholar 

  36. Wang, Y., Parkan, C.: A general multiple attribute decision-making approach for integrating subjective preferences and objective information. Fuzzy Sets Syst. 157(10), 1333–1345 (2006). https://doi.org/10.1016/j.fss.2005.11.017

    Article  MATH  Google Scholar 

  37. Wu, Y., Wang, R., Dai, W., Dong, S., You, X., You, H., Liu, L.: User portraits and investment planning based on accounting data. In: 2020 IEEE International Conference on Services Computing, SCC 2020, Beijing, China, November 7-11, 2020, pp. 404–411 (2020). https://doi.org/10.1109/SCC49832.2020.00059

  38. Wu, Y., Yu, P.: User portrait technology based on stacking mode. In: IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2020, Calgary, AB, Canada, August 17-22, 2020, pp. 245–250 (2020). https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00051

  39. Xue, G., Zhong, M., Li, J., Chen, J., Zhai, C., Kong, R.: Dynamic network embedding survey. arxiv:abs/2103.15447 (2021)

  40. Yang, Y., Guan, Z., Li, J., Zhao, W., Cui, J., Wang, Q.: Interpretable and efficient heterogeneous graph convolutional network. IEEE Transactions on Knowledge and Data Engineering, 1–1 (2021). https://doi.org/10.1109/TKDE.2021.3101356

  41. Zhang, Q., Wang, Y., Gong, Y., Huang, X.: Keyphrase extraction using deep recurrent neural networks on twitter. In: Su J., Carreras X., Duh K. (eds.) Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1-4, 2016, pp. 836–845. The Association for Computational Linguistics (2016). https://doi.org/10.18653/v1/d16-1080

  42. Zhang, F., Wang, Y., Liu, S., Wang, H.: Decision-based evasion attacks on tree ensemble classifiers. World Wide Web 23(5), 2957–2977 (2020). https://doi.org/10.1007/s11280-020-00813-y

    Article  Google Scholar 

  43. Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., Xu, B.: Joint extraction of entities and relations based on a novel tagging scheme. In: Barzilay R., Kan M. (eds.) Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers, pp. 1227–1236. Association for Computational Linguistics (2017). https://doi.org/10.18653/v1/P17-1113

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Acknowledgements

We would like to thank the anonymous reviewers for their insightful comments. This work was supported by the National Natural Science Foundation of China (No. 62176234, 62072409, 62176078, 61701443)

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

    Linan Zhu, Minhao Xu, Yifei Xu, Zhechao Zhu & Xiangjie Kong

  2. College of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Yanyan Zhao

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This article belongs to the Topical Collection: Special Issue on Decision Making in Heterogeneous Network Data Scenarios and Applications

Guest Editors: Jianxin Li, Chengfei Liu, Ziyu Guan, and Yinghui Wu

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Zhu, L., Xu, M., Xu, Y. et al. A multi-attribute decision making approach based on information extraction for real estate buyer profiling. World Wide Web 26, 187–205 (2023). https://doi.org/10.1007/s11280-022-01010-9

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