ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models

Autor:	Ghazimatin, Azin, Pramanik, Soumajit, Roy, Rishiraj Saha, Weikum, Gerhard
Rok vydání:	2021
Předmět:	Computer Science - Information Retrieval Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1145/3442381.3449848
Popis:	System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of the generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback. Comment: WWW 2021, 11 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2102.09388 Zobrazit plný text záznamu View this record from Arxiv