[1]Agrawal, R., A. Habeeb, and C. H. Hsueh. 2018. Learning User Intent from Action Sequences on Interactive Systems. In Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence.
[2]Batmaz, Z., A. Yurekli, A. Bilge, and C. Kaleli. 2019. A review on deep learning for recommender systems: challenges and remedies. Artificial Intelligence Review, 52(1), 1-37.
[3]Bouneffouf, D., A. Bouzeghoub, and A. L. Gançarski. 2012. A contextual-bandit algorithm for mobile context-aware recommender system. In International Conference on Neural Information Processing (pp. 324-331). Springer, Berlin, Heidelberg.
[4]Campos, P. G., F. Díez, and I. Cantador. 2014. Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Modelling and User-Adapted Interaction, 24(1-2), 67-119.
[5]Chen, X., H. Xu, Y. Zhang, J. Tang, Y. Cao, Z. Qin, and H. Zha. 2018. Sequential recommendation with user memory networks. In Proceedings of the eleventh ACM international conference on web search and data mining (pp. 108-116). ACM.
[6]Dai, H., Y. Wang, R. Trivedi, and L. Song. 2016. Deep coevolutionary network: Embedding user and item features for recommendation. arXiv preprint arXiv:1609.03675.
[7]Dallmann, A., A. Grimm, C. Pölitz, D. Zoller, and A. Hotho. 2017. Improving session recommendation with recurrent neural networks by exploiting dwell time. arXiv preprint arXiv:1706.10231.
[8]Devooght, R., and H. Bersini. 2017. Long and short-term recommendations with recurrent neural networks. In Proceedings of the 25th Conference on User Modelling, Adaptation and Personalization (pp. 13-21). ACM.
[9]Diwandari, S., A. E. Permanasari, and I. Hidayah. 2018. Research methodology for analysis of E-commerce user activity based on user interest using web usage mining. Journal of ICT Research and Applications, 12(1), 54-69.
[10]Donkers, T., B. Loepp, and J. Ziegler. 2017. Sequential user-based recurrent neural network recommendations. In Proceedings of the Eleventh ACM Conference on Recommender Systems (pp. 152-160). ACM.
[11]Du, Y., H. Liu, Y. Qu, and Z. Wu. 2018. Online Personalized Next-Item Recommendation via Long Short Term Preference Learning. In Pacific Rim International Conference on Artificial Intelligence (pp. 915-927). Springer, Cham.
[12]Fang, Z., L. Zhang, and K. Chen. 2016. Hybrid Recommender System Based on Personal Behavior Mining. arXiv preprint arXiv:1607.02754.
[13]Figueiredo, F., B. Ribeiro, J. M. Almeida, and C. Faloutsos. 2016. TribeFlow: Mining & predicting user trajectories. In Proceedings of the 25th international conference on world wide web (pp. 695-706). International World Wide Web Conferences Steering Committee.
[14]Gasmi, I., H. Seridi-Bouchelaghem, L. Hocine, and B. Abdelkarim. 2015. Collaborative filtering recommendation based on dynamic changes of user interest. Intelligent Decision Technologies, 9(3), 271-281.
[15]Gasparetti, F. 2017. Modeling user interests from web browsing activities. Data mining and knowledge discovery, 31(2), 502-547.
[16]Gholamian, M., M. Fathian, M. Julashokri , and A. Mehrbod. 2011. Improving electronic customers' profile in recommender systems using data mining techniques. Management Science Letters, 1(4), 449-456.
[17]Guha, R., V. Gupta, V. Raghunathan, and R. Srikant. 2015. User modeling for a personal assistant. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining (pp. 275-284). ACM.
[18]Gupta, S., and R. Mamtora. 2014. A survey on association rule mining in market basket analysis. International Journal of Information and Computation Technology, 4(4), 409-414.
[19]Hidasi, B., A. Karatzoglou, L. Baltrunas, and D. Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939.
[20]Hidasi, B., M. Quadrana, A. Karatzoglou, and D. Tikk. 2016. Parallel recurrent neural network architectures for feature-rich session-based recommendations. In Proceedings of the 10th ACM conference on recommender systems (pp. 241-248). ACM.
[21]Ho, S. Y., & Bodoff, D. (2014). The effects of Web personalization on user attitude and behaviour: An integration of the elaboration likelihood model and consumer search theory. MIS quarterly, 38(2).
[22]Hsieh, C. K., L. Yang, Y. Cui, T. Y. Lin, S. Belongie, and D. Estrin. 2017. Collaborative metric learning. In Proceedings of the 26th international conference on world wide web (pp. 193-201). International World Wide Web Conferences Steering Committee.
[23]Hu, L., L. Cao, S. Wang, G. Xu, J. Cao, and Z. Gu. 2017. Diversifying Personalized Recommendation with User-session Context. In IJCAI (pp. 1858-1864).
[24]Hu, Y., Y. Koren, and C. Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE International Conference on Data Mining (pp. 263-272). Ieee.
[25]Hwangbo, H., Y. S. Kim, and K. J. Cha. 2018. Recommendation system development for fashion retail e-commerce. Electronic Commerce Research and Applications, 28, 94-101.
[26]Jain, D., A. R. Sinha, D. Gupta, N. Sheoran, and S. Khosla. 2018. Measurement of Users’ Experience on Online Platforms from Their Behavior Logs. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 475-487). Springer, Cham.
[27]Jannach, D., L. Lerche, and M. Jugovac. 2015. Adaptation and evaluation of recommendations for short-term shopping goals. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 211-218). ACM.
[28]Jannach, D., M. Ludewig, and L. Lerche. 2017. Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts. User Modeling and User-Adapted Interaction, 27(3-5), 351-392.
[29]Jasek, P., L. Vrana, L. Sperkova, Z. Smutny, and M. Kobulsky. 2019. Comparative analysis of selected probabilistic customer lifetime value models in online shopping. Journal of Business Economics and Management, 20(3), 398-423.
[30]Jiacheng, X. 2017. Family shopping recommendation system using user profile and behaviour data. arXiv preprint arXiv:1708.07289.
[31]Jing, H., and A. J. Smola. 2017. Neural survival recommender. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (pp. 515-524). ACM.
[32]Kala, K. U., and M. Nandhini. (2018). Scope of context awareness in cross domain recommender system–a brief review. International Journal of Engineering & Technology, 7(4), 5570-5579.
[33]Kaur, M., and S. Kang. 2016. Market Basket Analysis: Identify the changing trends of market data using association rule mining. Procedia computer science, 85, 78-85.
[34]Ko, Y. J., L. Maystre, and M. Grossglauser. 2016. Collaborative recurrent neural networks for dynamic recommender systems. In Journal of Machine Learning Research: Workshop and Conference Proceedings (Vol. 63, No. CONF).
[35]Kong, W., R. Li, J. Luo, A. Zhang, Y. Chang, and J. Allan. 2015. Predicting search intent based on pre-search context. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 503-512). ACM.
[36]Krishna, K., D. Jain, S. V. Mehta, and S. Choudhary. 2018. An lstm based system for prediction of human activities with durations. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(4), 147.
[37]Kumar, V., D. Khattar, S. Gupta, M. Gupta, and V. Varma. 2017. Deep Neural Architecture for News Recommendation. In CLEF (Working Notes).
[38]Kurniawan, F., B. Umayah, J. Hammad, S. M. S. Nugroho, and M. Hariadi. 2017. Market Basket Analysis to identify customer behaviours by way of transaction data. Knowledge Engineering and Data Science, 1(1), 20-25.
[39]Lang, T., and M. Rettenmeier. 2017. Understanding consumer behaviour with recurrent neural networks. In Workshop on Machine Learning Methods for Recommender Systems.
[40]Li, Z., H. Zhao, Q. Liu, Z. Huang, T. Mei, and E. Chen. 2018. Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviours. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1734-1743). ACM.
[41]Lipton, Z. C. 2016. The mythos of model interpretability. arXiv preprint arXiv:1606.03490.
[42]Liu, Q., S. Wu, D. Wang, Z. Li, and L. Wang. 2016. Context-aware sequential recommendation. In 2016 IEEE 16th International Conference on Data Mining (ICDM) (pp. 1053-1058). IEEE.
[43]Liu, Y., X. Xie, C. Wang, J. Y. Nie, M. Zhang, and S. Ma. 2017. Time-aware click model. ACM Transactions on Information Systems (TOIS), 35(3), 16.
[44]Liu, Z., Y. Wang, M. Dontcheva, M. Hoffman, S. Walker, and A. Wilson. 2016a. Patterns and sequences: Interactive exploration of clickstreams to understand common visitor paths. IEEE Transactions on Visualization and Computer Graphics, 23(1), 321-330.
[45]Loyola, P., C. Liu, and Y. Hirate. 2017. Modeling user session and intent with an attention-based encoder-decoder architecture. In Proceedings of the Eleventh ACM Conference on Recommender Systems (pp. 147-151). ACM.
[46]Matsubara, Y., Y. Sakurai, C. Faloutsos, T. Iwata, and M. Yoshikawa. 2012. Fast mining and forecasting of complex time-stamped events. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 271-279). ACM.
[47]Park, Y. J., and K. N. Chang. 2009. Individual and group behaviour-based customer profile model for personalized product recommendation. Expert Systems with Applications, 36(2), 1932-1939.
[48]Parrish, R., H. Manning, B. Salamin, and S. Neuburg. 2019. Forrester's Top Customer Experience Research Findings Of 2018. Forrester. Retrieved June 20, 2019, from https://www.forrester.com/report/Forresters+Top+Customer+Experience+Research+Findings+Of+2018/-/E-RES150115
[49]Pereira, L. M. 2013. State-of-the-art of intention recognition and its use in decision making. AI Communications, 26(2), 237-246.
[50]Phuong, T. M., T. C. Thanh, and N. X. Bach. 2018. Combining User-Based and Session-Based Recommendations with Recurrent Neural Networks. In International Conference on Neural Information Processing (pp. 487-498). Springer, Cham.
[51]Pillai, J., and O. P. Vyas. 2011. User centric approach to itemset utility mining in Market Basket Analysis. International Journal Computer Science & Engineering, 3(1), 393-400.
[52]Quadrana, M., A. Karatzoglou, B. Hidasi, and P. Cremonesi. 2017. Personalizing session-based recommendations with hierarchical recurrent neural networks. In Proceedings of the Eleventh ACM Conference on Recommender Systems (pp. 130-137). ACM.
[53]Quadrana, M., P. Cremonesi, and D. Jannach, D. 2018. Sequence-aware recommender systems. ACM Computing Surveys (CSUR), 51(4), 66.
[54]Raorane, A. A., R. V. Kulkarni, and B. D. Jitkar. 2012. Association rule–extracting knowledge using market basket analysis. Research Journal of Recent Sciences.
[55]Raza, S., and C. Ding. 2019. Progress in context-aware recommender systems—an overview. Computer Science Review, 31, 84-97.
[56]Ricci F., L. Rokach, and B. Shapira. 2015. Recommender Systems Handbook (2nd ed.). Springer Publishing Company, Incorporated.
[57]Romov, P., and E. Sokolov. 2015. Recsys challenge 2015: ensemble learning with categorical features. In Proceedings of the 2015 International ACM Recommender Systems Challenge (p. 1). ACM.
[58]Royston-Webb, T. 2018. Propensity Modelling for Business. A Data Science Foundation White Paper. https://datascience.foundation/downloadpdf/26/whitepaper (accessed June 20, 2019).
[59]Salonen, V., and H. Karjaluoto. 2016. Web personalization: the state of the art and future avenues for research and practice. Telematics and Informatics, 33(4), 1088-1104.
[60]Sheil, H., and O. Rana. 2017. Classifying and recommending using gradient boosted machines and vector space models. In UK Workshop on Computational Intelligence (pp. 214-221). Springer, Cham.
[61]Sheil, H., O. Rana, and R. Reilly. 2018. Predicting purchasing intent: automatic feature learning using recurrent neural networks. arXiv preprint arXiv:1807.08207.
[62]Sheil, H., O. Rana, and R. Reilly. 2018a. Understanding ecommerce clickstreams: a tale of two states. KDD Deep Learning Day. ACM.
[63]Shi, F., C. Ghedira, and J. L. Marini. 2015. Context adaptation for smart recommender systems. IT Professional, 17(6), 18-26.
[64]Sinha, A. R., D. Jain, N. Sheoran, S. Khosla, and R. Sasidharan. 2019. Surveys Without Questions: A Reinforcement Learning Approach.
[65]Smirnova, E., and F. Vasile. 2017. Contextual sequence modeling for recommendation with recurrent neural networks. In Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems (pp. 2-9). ACM.
[66]Su, Q., and L. Chen. 2015. A method for discovering clusters of e-commerce interest patterns using click-stream data. Electronic commerce research and applications, 14(1), 1-13.
[67]Su, X., and T. M. Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009.
[68]Tan, Y. K., X. Xu, and Y. Liu. 2016. Improved recurrent neural networks for session-based recommendations. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (pp. 17-22). ACM.
[69]Tang, J., and K. Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (pp. 565-573). ACM.
[70]Tavakol, M., and U. Brefeld. 2014. Factored MDPs for detecting topics of user sessions. In Proceedings of the 8th ACM Conference on Recommender Systems (pp. 33-40). ACM.
[71]Tay, Y., L. Anh Tuan, and S. C. Hui. 2018. Latent relational metric learning via memory-based attention for collaborative ranking. In Proceedings of the 2018 World Wide Web Conference (pp. 729-739). International World Wide Web Conferences Steering Committee.
[72]Tkachenko, Y. 2015. Autonomous CRM control via CLV approximation with deep reinforcement learning in discrete and continuous action space. arXiv preprint arXiv:1504.01840.
[73]Toth, A., L. Tan, G. Di Fabbrizio, and A. Datta. 2017. Predicting Shopping Behavior with Mixture of RNNs. In eCOM@ SIGIR.
[74]Tuan, T. X., and T. M. Phuong. 2017. 3D convolutional networks for session-based recommendation with content features. In Proceedings of the Eleventh ACM Conference on Recommender Systems (pp. 138-146). ACM.
[75]Twardowski, B. 2016. Modelling contextual information in session-aware recommender systems with neural networks. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 273-276). ACM.
[76]Villegas, N. M., C. Sánchez, J. Díaz-Cely, and G. Tamura. 2018. Characterizing context-aware recommender systems: A systematic literature review. Knowledge-Based Systems, 140, 173-200.
[77]Wang, G., X. Zhang, S. Tang, C. Wilson, H. Zheng, and B. Y. Zhao. 2017. Clickstream user behaviour models. ACM Transactions on the Web (TWEB), 11(4), 21.
[78]Wang, J., and Y. Zhang. 2013. Opportunity model for e-commerce recommendation: right product; right time. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval (pp. 303-312). ACM.
[79]Wang, S., L. Hu, L. Cao, X. Huang, D. Lian, and W. Liu. 2018. Attention-based transactional context embedding for next-item recommendation. In Thirty-Second AAAI Conference on Artificial Intelligence.
[80]Wang, X., L. Yu, K. Ren, G. Tao, W. Zhang, Y. Yu, and J. Wang. 2017a. Dynamic attention deep model for article recommendation by learning human editors' demonstration. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 2051-2059). ACM.
[81]Wang, Y., N. Du, R. Trivedi, and L. Song. 2016. Coevolutionary latent feature processes for continuous-time user-item interactions. In Advances in Neural Information Processing Systems (pp. 4547-4555).
[82]Westreich, D., J. Lessler, and M. J. Funk. 2010. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. Journal of clinical epidemiology, 63(8), 826-833.
[83]White, R. W., P. N. Bennett, and S. T. Dumais. 2010. Predicting short-term interests using activity-based search context. In Proceedings of the 19th ACM international conference on Information and knowledge management (pp. 1009-1018). ACM.
[84]Wu, C. Y., A. Ahmed, A. Beutel, A. J. Smola, and H. Jing. 2017. Recurrent recommender networks. In Proceedings of the tenth ACM international conference on web search and data mining (pp. 495-503). ACM.
[85]Wu, S., W. Ren, C. Yu, G. Chen, D. Zhang, and J. Zhu. 2016. Personal recommendation using deep recurrent neural networks in NetEase. In 2016 IEEE 32nd International Conference on Data Engineering (ICDE) (pp. 1218-1229). IEEE.
[86]Wu, W., J. Zhao, C. Zhang, F. Meng, Z. Zhang, Y. Zhang, and Q. Sun. 2017a. Improving performance of tensor-based context-aware recommenders using Bias Tensor Factorization with context feature auto-encoding. Knowledge-Based Systems, 128, 71-77.
[87]Wu, Z., B. H. Tan, R. Duan, Y. Liu, and R. S. Mong Goh. 2015. Neural modeling of buying behaviour for e-commerce from clicking patterns. In Proceedings of the 2015 International ACM Recommender Systems Challenge (p. 12). ACM.
[88]Yi, X., L. Hong, E. Zhong, N. N. Liu, and S. Rajan. 2014. Beyond clicks: dwell time for personalization. In Proceedings of the 8th ACM Conference on Recommender systems (pp. 113-120). ACM.
[89]Zhang H., W. Ni, X. Li, and Y. Yang. 2016. Modeling the heterogeneous duration of user interest in time-dependent recommendation: A hidden semi-Markov approach. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(2), 177-194.
[90]Zhang S., Y. Tay, L. Yao, and A. Sun. (2019) Next item recommendation with self-attention. arXiv: Information Retrieval.
[91]Zhang S., L. Yao, A. Sun, and Y. Tay. 2019a. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR), 52(1), 5.
[92]Zhao, Q., and S. S. Bhowmick. 2003. Association rule mining: A survey. Nanyang Technological University, Singapore.
[93]Zheng, Y., and A. A. Jose. 2019. Context-aware recommendations via sequential predictions. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (pp. 2525-2528). ACM.
[94]Zhou, T., H. Qian, Z. Shen, C. Zhang, C. Wang, S. Liu, and W. Ou. 2018. JUMP: a joint predictor for user click and dwell time. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. AAAI Press (pp. 3704-3710).
[95]Zhu, Y., H. Li, Y. Liao, B. Wang, Z. Guan, H. Liu, and D. Cai. 2017. What to Do Next: Modeling User Behaviors by Time-LSTM. In IJCAI (pp. 3602-3608).