Transparent and Accountable Recommender Systems

Speaker:  Alan Said – Gothenburg, Sweden
Topic(s):  Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science

Abstract

Recommender systems increasingly mediate interactions across digital platforms, from streaming services to e-commerce. As their use expands, ensuring these systems are transparent and accountable becomes critical. Developing methods for understanding recommendation outcomes, explaining algorithmic decisions, and systematically addressing bias are key challenges faced by researchers and practitioners. The talk covers approaches for enhancing the transparency of recommender systems, including recent advances in explainability using large language models, methods for reproducibility in recommendation research, and best practices for accountability. Real-world examples highlight both the challenges and potential solutions for ensuring recommendations are not only effective, but also equitable and comprehensible to end users.

About this Lecture

Number of Slides:  50 - 70
Duration:  45 - 70 minutes
Languages Available:  English
Last Updated: 

Request this Lecture

To request this particular lecture, please complete this online form.

Request a Tour

To request a tour with this speaker, please complete this online form.

All requests will be sent to ACM headquarters for review.