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Abhishek Srivastava

Abhishek Srivastava

Assistant Professor
Information Systems
abhishek[at]iimv[dot]ac[dot]in



Prof. Abhishek Srivastava holds a PhD degree from IIM Ranchi. Prior to his PhD, he worked with Adobe Systems and Verizon in various capacities. Prior to joining IIM Visakhapatnam, he worked as Assistant Professor at IIM Jammu and as an Independent AI & IT consultant for various domestic and international clients. 

His research interests lie in the areas of Personalization using Recommender systems, Privacy, HCI & AI Auditing.

At MBA and Executive MBA level:  

Core courses - 
  • Business Analytics  
  • Spreadsheet Modelling  
  • Business Modelling  
Elective courses -  
  • Digital Transformation using AI & Emerging Technologies 
  • Cognitive Computing and Neuro Management   
  • Business Intelligence, Social Media and Cognitive Analytics 
  • E-Commerce 
  • Advanced Data Science and Machine Learning 
  • People Analytics 
He has also conducted multiple sessions in various MDPs/FDPs related to the areas of Technology Management, Data Science, Artificial Intelligence, Emerging Technologies, Research Methodologies and Entrepreneurship. 

Journal 

  • Srivastava, A., Bala, P. K., & Kumar, B. (2020). New perspectives on gray sheep behavior in E-commerce recommendations. Journal of Retailing and Consumer Services, 53. (ABDC A) 
  • Srivastava, A., Dasgupta, S. A., Ray, A., Bala, P. K., & Chakraborty, S. (2021). Relationships between the “Big Five” personality types and consumer attitudes in Indian students toward augmented reality advertising. Aslib Journal of Information Management. (ABDC B) 
  • Ray, A., Bala, P. K., Dasgupta, S. A., & Srivastava, A. (2020). Understanding the factors influencing career choices in India: from the students' perspectives. International Journal of Indian Culture and Business Management, 20(2), 175-193. (ABS)
  • Srivastava, A., Bala, P. K., & Kumar, B. (2017). Transfer learning for resolving sparsity problem in recommender systems: human values approach. JISTEM-Journal of Information Systems and Technology Management, 14(3), 323-337.(ABDC C) 
  •  Kumar, B., Bala, P. K., & Srivastava, A. (2016). Cosine based latent factor model for precision oriented recommendation. International Journal of Advanced Computer Science and Applications, 7(1), 451-457. (Scopus)
Conference 

  • Landia, N., Cheung, F., North, D., Kalloori, S., Srivastava, A., & Ferwerda, B. (2022, September). RecSys Challenge 2022: Fashion Purchase Prediction. In Proceedings of the 16th ACM Conference on Recommender Systems (pp. 694-697). 
  • Landia, N., Mcalister, R., North, D., Kalloori, S., Srivastava, A., & Ferwerda, B. (2022). RecSys Challenge 2022 Dataset: Dressipi 1M Fashion Sessions. In Proceedings of the Recommender Systems Challenge 2022 (pp. 1-3) 
  • Kumar Bipul , Bala, P. K , Ray A, Srivastava A. (2019) User item context interacting for enhancing eCommerce data management , Proceedings of 6th International Conference on Business Analytics and Intelligence (ICBAI), Indian Institute of Management, Bangalore, India 
  • Srivastava A., & Bala, P. K. (2018). Diffusing Indian Vedic Philosophy for Transfer Learning in Artificial Intelligence Systems for Personalised Recommendation. Proceedings of 6th International Conference on Business Analytics and Intelligence (ICBAI), Indian Institute of Science, Bangalore, India 
  • Srivastava, A. (2016, September). Gray sheep, influential users, user modeling and recommender system adoption by startups. In Proceedings of the 10th ACM conference on recommender systems (pp. 443-446). MIT/IBM Research, Boston, USA
  • Kumar, R., Bala, P.K., Varma, N. and Srivastava, A., (2015). A framework for simple, secure and cost effective online voting system. In European Conference on Digital Government (p. 158). University of Portsmouth, UK
  • Srivastava, A, Kumar Bipul , Bala, P. K (2015). Enhancing Recommender systems accuracy by using user-items latent features similarity. WEI International Academic Conference, Harvard, Boston, USA 
  • Srivastava, A., Kumar Bipul , Bala, P. K (2015). An Improvised Latent Factor Model For More Efficient Recommender Systems. International Conference for Business and Economics, Harvard, Boston, USA

  • As program co-director, designed and co-ordinated two long terms executive education programs, one in general management and another in Artificial Intelligence and Machine Learning 
  • Recently took a guest lecture session on theme Personalization in the era of Artificial Intelligence, as part of the European FOReSIGHT Project, hosted by The Bucharest University of Economic Studies, Bucharest, Romania
  • Conducted workshops and MDP/FDP sessions on topics related to   of Technology Management, Data Science, Artificial Intelligence, Business Analytics, Emerging Technologies, Research Methodologies and Entrepreneurship.

  • Chair – Information Systems Area 
  • Member- PGP Committee

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Andhra University Campus, Visakhapatnam – 530 003.

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