The 2nd Workshop on Recommendation with Generative Models

TL;DR

This paper outlines the scope, rationale, and operational plan for the 2nd Workshop on Recommendation with Generative Models, emphasizing five pillars: improved algorithms, content generation, interaction paradigms, trust, and evaluation. It argues that generative models, including LLMs and diffusion models, can enhance user modeling, item content generation, and conversational interfaces while raising concerns about bias, privacy, and safety. The CFP details topics, submission guidelines, and a half-day program featuring keynotes, papers, and a panel, with eight papers accepted from ten submissions. The workshop aims to catalyze collaboration across academia and industry, spurring next-generation recommender systems and informing future dissemination through venues like ACM TOIS.

Abstract

The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations. This workshop serves as a platform for researchers to explore and exchange innovative concepts related to the integration of generative models into recommender systems. It primarily focuses on five key perspectives: (i) improving recommender algorithms, (ii) generating personalized content, (iii) evolving the user-system interaction paradigm, (iv) enhancing trustworthiness checks, and (v) refining evaluation methodologies for generative recommendations. With generative models advancing rapidly, an increasing body of research is emerging in these domains, underscoring the timeliness and critical importance of this workshop. The related research will introduce innovative technologies to recommender systems and contribute to fresh challenges in both academia and industry. In the long term, this research direction has the potential to revolutionize the traditional recommender paradigms and foster the development of next-generation recommender systems.