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Active Management of Complex Non-self finalising Behaviours through Deep Analytics

Funding: 2015: $120,000
2016: $120,000
2017: $120,000

Project Member(s): Xu, G.

Funding or Partner Organisation: Arizona State University
Australian Research Council (ARC Linkage Projects)
Australian Taxation Office

Start year: 2015

Summary: This project will build theoretical breakthroughs and novel tools for deep analytics and active management of non-self finalising (NSF) individual and business behaviours, which are sophisticated and increasingly seen in the public sector such as taxation and business including banking and insurance. The challenging economic environment continues to make managing NSF behaviours challenging. To date there are no sufficient theory and effective systems in data mining and behaviour science to systematically learn the intent, impact and patterns of and to suggest cost-effective responses on NSF behaviours. This project will ensure Australia¿s leading role in innovation for evidence-driven enterprise behaviour analytics and management.

Publications:

Saeed, Z, Abbasi, RA, Maqbool, O, Sadaf, A, Razzak, I, Daud, A, Aljohani, NR & Xu, G 2019, 'What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter', Journal of Grid Computing, vol. 17, no. 2, pp. 279-312.
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Saeed, Z, Abbasi, RA, Razzak, I, Maqbool, O, Sadaf, A & Xu, G 2019, 'Enhanced Heartbeat Graph for emerging event detection on Twitter using time series networks', Expert Systems with Applications, vol. 136, pp. 115-132.
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Saeed, Z, Ayaz Abbasi, R, Razzak, MI & Xu, G 2019, 'Event Detection in Twitter Stream Using Weighted Dynamic Heartbeat Graph Approach [Application Notes]', IEEE Computational Intelligence Magazine, vol. 14, no. 3, pp. 29-38.
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Liu, Q, Wu, R, Chen, E, Xu, G, Su, Y, Chen, Z & Hu, G 2018, 'Fuzzy cognitive diagnosis for modelling examinee performance', ACM Transactions on Intelligent Systems and Technology, vol. 9, no. 4.
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Wan, Y, Xu, G, Chen, L, Zhao, Z & Wu, J 2018, 'Exploiting cross-source knowledge for warming up community question answering services', Neurocomputing, vol. 320, pp. 25-34.
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Wang, D, Deng, S & Xu, G 2018, 'Sequence-based context-aware music recommendation', Information Retrieval Journal, vol. 21, no. 2-3, pp. 230-252.
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Wang, D, Deng, S, Zhang, X & Xu, G 2018, 'Learning to embed music and metadata for context-aware music recommendation', World Wide Web, vol. 21, pp. 1399-1423.
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Hu, L, Cao, L, Cao, J, Gu, Z, Xu, G & Wang, J 2017, 'Improving the Quality of Recommendations for Users and Items in the Tail of Distribution', ACM Transactions on Information Systems, vol. 35, no. 3.
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Hu, L, Cao, L, Wang, S, Xu, G, Cao, J & Gu, Z 2017, 'Diversifying personalized recommendation with user-session context', IJCAI International Joint Conference on Artificial Intelligence, International Joint Conferences on Artifical Intelligence, International Joint Conferences on Artificial Intelligence Organization, Melbourne, Australia, pp. 1858-1864.
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Liu, S, Pang, N, Xu, G & Liu, H 2017, 'Collaborative Filtering via Different Preference Structures', International Conference on Knowledge Science, Engineering and Management, Springer, Melbourne, Australia, pp. 309-321.
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He, W & Xu, G 2016, 'Social media analytics: unveiling the value, impact and implications of social media analytics for the management and use of online information', Online Information Review, vol. 40, no. 1.
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Li, F, Xu, G & Cao, L 2016, 'Two-level matrix factorization for recommender systems', Neural Computing and Applications, vol. 27, no. 8, pp. 2267-2278.
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Xu, G, Fu, B & Gu, Y 2016, 'Point-of-Interest Recommendations via a Supervised Random Walk Algorithm', IEEE Intelligent Systems, vol. 31, no. 1, pp. 15-23.
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Yi, X, Paulet, R, Bertino, E & Xu, G 2016, 'Private Cell Retrieval from Data Warehouses', IEEE Transactions on Information Forensics and Security, vol. 11, no. 6, pp. 1346-1361.
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Chen, Y, Li, X, Li, L, Liu, G & Xu, G 2016, 'Modeling user mobility via user psychological and geographical behaviors towards point of-interest recommendation', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Database Systems for Advanced Applications, Springer, Dallas, Texas, USA, pp. 364-380.
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Wang, D, Deng, S, Zhang, X & Xu, G 2016, 'Learning music embedding with metadata for context aware recommendation', Proceedings of the 2016 ACM International Conference on Multimedia Retrieval, ACM International Conference on Multimedia Retrieval, ACM, New York, USA, pp. 249-253.
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Li, X, Xu, G, Chen, E & Li, L 2015, 'Learning User Preferences across Multiple Aspects for Merchant Recommendation', Proceedings of the 2015 IEEE International Conference on Data Mining, IEEE International Conference on Data Mining, IEEE, Atlantic City, NJ, pp. 865-870.
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Li, X, Xu, G, Chen, E & Li, L 2015, 'MARS: A multi-aspect Recommender system for Point-of-Interest', Proceedings of the 2015 IEEE 31st International Conference on Data Engineering (ICDE), IEEE International Conference on Data Engineering, IEEE, Seoul, Korea, pp. 1436-1439.
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Medvediev, K, Xu, G, Berkovsky, S & Onikienko, Y 2015, 'An analysis of new visitors' website behaviour before & after TV advertising', Proceedings of the 2015 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC),, International Conference on Behavioral, Economic and Socio-cultural Computing, IEEE, Nanjing, China, pp. 109-115.
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Keywords: behavior analytics, behavior informatics

FOR Codes: Application Tools and System Utilities, Pattern Recognition and Data Mining, Information Processing Services (incl. Data Entry and Capture), Simulation and Modelling