Year : 2023
On Diverse and Serendipitous Item Recommendation: A Reinforced Similarity and Multi-objective Optimization-Based Composite Recommendation Framework
Cite this Research Publication : Shrivastava, R., Sisodia, D.S., Nagwani , N.K. (2023). On Diverse and Serendipitous Item Recommendation: A Reinforced Similarity and Multi- objective Optimization-Based Composite Recommendation Framework. Lecture Notes in Electrical Engineering, vol 997. Springer, Singapore. https://doi.org/10.1007/978-981-99-0085-5_1 . (Scopus Indexed)
Publisher : SpringerLink
Year : 2022
Fair Exposure: A Multi-stakeholder Personalized Recommendation System Based on Multi-objective Optimization
Cite this Research Publication : Shrivastava, R., Sisodia, D.S., Nagwani , N.K. (2022). Fair Exposure: A Multi-stakeholder Personalized Recommendation System Based on Multi- objective Optimization. In : , Lecture Notes in Networks and Systems, vol 303. Springer, Cham. https://doi.org/10.1007/978-3-030-86223-7_18 . (Scopus Indexed)
Publisher : SpringerLink
Year : 2022
Utility optimization-based multi-stakeholder personalized recommendation system
Cite this Research Publication : R. Shrivastava, D.S. Sisodia, N.K. Nagwani , Utility optimization-based multi-stakeholder personalized recommendation system, Data Technologies and Applications (2022). https://doi.org/10.1108/DTA-07-2021-0182 . (SCI-E, Impact Factor:1.667).
Publisher : Emerald logo
Year : 2022
An optimized recommendation framework exploiting textual review based opinion mining for generating pleasantly surprising, novel yet relevant recommendations
Cite this Research Publication : R. Shrivastava, D.S. Sisodia, N.K. Nagwani , U.R. BP, An optimized recommendation framework exploiting textual review based opinion mining for generating pleasantly surprising, novel yet relevant recommendations, Pattern Recognittion Letters 159 (2022) 91–99. https://doi.org/10.1016/j.patrec.2022.05.003 . ( SCI Impact Factor: 5.1).
Publisher : Elsevier
Year : 2022
Deep neural network-based multi-stakeholder recommendation system exploiting multi-criteria ratings for preference learning
Cite this Research Publication : R. Shrivastava, D. Singh Sisodia, N. Kumar Nagwani , Deep neural network-based multi-stakeholder recommendation system exploiting multi-criteria ratings for preference learning, Expert Systems with Application 213 (2023) 119071. https://doi.org/https://doi.org/10.1016/j.eswa.2022.119071 . (SCI- E Impact Factor: 8.5)
Publisher : Elsevier