We consider the problem of supporting intuitive explanations in recommender systems used for automated career counseling. Explanations enhance the transparency in operation of a recommender system and facilitate user-acceptance, adoption, and trust in the system. We leverage the inference Web (IW) Infrastructure and the proof markup language (PML) as a foundation for supporting intuitive explanations in recommender systems for automated career counseling. We present the design and implementation of our system, highlighting the salient features of our approach using an illustrative example.
['Tejaswini Narayanan', 'Deborah L. McGuinness']
Towards Leveraging Inference Web to Support Intuitive Explanations in Recommender Systems for Automated Career Counseling