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Variational Stackelberg Discrete Diffusion substantially improves molecular validity over uniform and masked diffusion, reduces text perplexity relative to uniform diffusion while remaining competitive with masked diffusion, and achieves sizable improvements in offline playlist recommendation metrics.
Textual User Taste is presented, a system that generates structured natural-language taste profiles from listening behavior, interaction signals, content metadata, and optional user feedback, and deploys them to millions of Spotify users, revealing that taste profiles support positive natural-language steering, while e...
This work provides a practical framework for accelerating the development of conversational recommendation agents in industry, addressing challenges that are becoming increasingly central to recommender systems and agentic applications as natural-language interfaces reach widespread adoption.
It is found that LLM judges are influenced by descriptions of a recommendation algorithm’s optimization objective, even when evaluating identical recommendation outputs, a phenomenon the authors term intent-description anchoring bias.
GLIDE is a production-scale generative recommender for podcast discovery at Spotify that increases non-habitual podcast streaming on Spotify home surface by up to 5.4% and new-show discovery by up to 14.3%, while meeting production cost and latency constraints.
NEO is introduced, a framework that adapts a pre-trained decoder-only LLM into a tool-free, catalog-grounded generator and treats SIDs as a distinct modality and study design choices for integrating discrete entity representations into LLMs via staged alignment and instruction tuning.
Large language models (LLMs) are increasingly applied to recommendation, retrieval, and reasoning, yet deploying a single end-to-end model that can jointly support these behaviors over large, heterogeneous catalogs remains challenging. Such systems must generate unambiguous references to real items, handle multiple ent...
It is found that introducing explicit descriptive reasoning traces reduces traditional offline recommendation effectiveness under standard SFT and RL training, even though natural language titles produce substantially more grounded and interpretable traces.
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