Punam Bedi

dblp:25/4147 · DBLP profile ↗
← Back
6ranked-venue papers in the field
2as first author
4since 2021 · last 2026
0000-0002-6007-7961ORCID · corroborated

Domains — venue-derived; a paper can count in several

Knowledge Engineering, Semantic Web & Information Systems · 4 (1 first)Data Mining & Knowledge Discovery · 2 (1 first)
YearPublicationVenuePosition
2026 A contextual bandits framework using Siamese architecture for group reciprocal recommendations
Tulika Kumari, Bhavna Gupta, Ravish Sharma, Punam Bedi
Knowl. Inf. Syst.4
2025 XLR-KGDD: leveraging LLM and RAG for knowledge graph-based explainable disease diagnosis using multimodal clinical information
Punam Bedi, Anjali Thukral, Shivani Dhiman
Knowl. Inf. Syst.1
2024 Session-aware recommender system using double deep reinforcement learning
Purnima Khurana, Bhavna Gupta, Ravish Sharma, Punam Bedi
J. Intell. Inf. Syst.4
2022 A contextual-bandit approach for multifaceted reciprocal recommendations in online dating
Tulika Kumari, Ravish Sharma, Punam Bedi
J. Intell. Inf. Syst.3
2014 Empowering recommender systems using trust and argumentation
Punam Bedi, Pooja Bhatt Vashisth
Inf. Sci.1
2012 Building Socially-Aware E-Learning Systems Through Knowledge Management
abstract
Conformance to social context while designing an e-learning course is crucial in enhancing acceptability of the course. Building socially aware e-learning courses requires elicitation of social opinion from various stakeholders associated with the system. Stakeholders are disparate in their perception towards the intricacies of the system, leading to generation of numerous assorted ideas. Knowledge Management (KM) assimilates these ideas to bring congruency into the system. This paper proposes i) a model KMeLS (Knowledge Management in e-Learning Systems) built upon the SECI (Socialization, Externalization, Combination and Internalization) framework, and ii) an algorithm PARSeL (Prioritizing Alternatives using Recommendations of Stakeholders in e-Learning) to incorporate KM into designing an e-learning course. PARSeL prioritizes the content using stakeholder recommendations using Analytic Hierarchy Process (AHP) and fuzzy modeling. A case study is also presented with a goal of prioritizing a set of programming languages for an online computing course. The proposed methodology can be promising in recommending appropriate content for the e-learners and can be implemented to benefit e-learning organizations in a wider spectrum.
Hema Banati, Punam Bedi
Int. J. Knowl. Manag.3