all AI news
Stability of Explainable Recommendation
May 6, 2024, 4:42 a.m. | Sairamvinay Vijayaraghavan, Prasant Mohapatra
cs.LG updates on arXiv.org arxiv.org
Abstract: Explainable Recommendation has been gaining attention over the last few years in industry and academia. Explanations provided along with recommendations in a recommender system framework have many uses: particularly reasoning why a suggestion is provided and how well an item aligns with a user's personalized preferences. Hence, explanations can play a huge role in influencing users to purchase products. However, the reliability of the explanations under varying scenarios has not been strictly verified from an …
abstract academia arxiv attention cs.ir cs.lg framework industry personalized reasoning recommendation recommendations stability type
More from arxiv.org / cs.LG updates on arXiv.org
Efficient Data-Driven MPC for Demand Response of Commercial Buildings
2 days, 22 hours ago |
arxiv.org
Testing the Segment Anything Model on radiology data
2 days, 22 hours ago |
arxiv.org
Calorimeter shower superresolution
2 days, 22 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US