Personalized Recommendations using Ecological Momentary Assessment

In the following article, I position my understanding of personalized recommendations considering diverse, ambiguous, and sparse environmental variables at personal- and population-level. I try to motivate the essence of homogenizing knowledge graphs with deep nets for weighing specific variables influencing decisions of stakeholders.

At broader-level, it demands, Knowledge infused Learning:

• Kursuncu, Ugur, Manas Gaur, and Amit Sheth. “Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning.” arXiv preprint arXiv:1912.00512 (2020) (Appeared in AAAI Spring Symposium).
• Gaur, Manas, Ugur Kursuncu, and Ruwan Wickramarachchi. “Shades of Knowledge-Infused Learning for Enhancing Deep Learning.” IEEE Internet Computing,(2019).
• Wickramarachchi, Ruwan, Cory Henson, and Amit Sheth. “An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice.” arXiv preprint arXiv:2003.00344 (2020).
• Gyrard, Amelie, Manas Gaur, Saeedeh Shekarpour, Krishnaprasad Thirunarayan, and Amit Sheth. “Personalized Health Knowledge Graph.” In ISWC Contextualized Knowledge Graph Workshop (2018).
• Kapanipathi, Pavan, Veronika Thost, Siva Sankalp Patel, Spencer Whitehead, Ibrahim Abdelaziz, Avinash Balakrishnan, Maria Chang et al. “Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks.” arXiv preprint arXiv:1911.02060 (2019).
• Kokel, Harsha, Phillip Odom, Shuo Yang, and Sriraam Natarajan. “A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains.”, In AAAI NYC, NY (2020).