1 citations · 2 across the 15 of their papers we have counts for
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AudioBoost: Increasing Audiobook Retrievability in Spotify Search with Synthetic Query Generation
Enrico Palumbo, Gustavo Penha, Alva Liu +6
Spotify has recently introduced audiobooks as part of its catalog, complementing its music and podcast offering. Search is often the first entry point for users to access new items…
Semantic IDs for Joint Generative Search and Recommendation
Gustavo Penha, Edoardo D'Amico, Marco De Nadai +8
Generative models powered by Large Language Models (LLMs) are emerging as a unified solution for powering both recommendation and search tasks. A key design choice in these models…
Aligned Query Expansion: Efficient Query Expansion for Information Retrieval through LLM Alignment
Adam Yang, Gustavo Penha, Enrico Palumbo +1
With the breakthroughs in large language models (LLMs), query generation techniques that expand documents and queries with related terms are becoming increasingly popular in the in…
Contextualizing Spotify's Audiobook List Recommendations with Descriptive Shelves
Gustavo Penha, Alice Wang, Martin Achenbach +7
In this paper, we propose a pipeline to generate contextualized list recommendations with descriptive shelves in the domain of audiobooks. By creating several shelves for topics th…
Text2Tracks: Prompt-based Music Recommendation via Generative Retrieval
Enrico Palumbo, Gustavo Penha, Andreas Damianou +5
In recent years, Large Language Models (LLMs) have enabled users to provide highly specific music recommendation requests using natural language prompts (e.g. "Can you recommend so…
Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other?
Gustavo Penha, Ali Vardasbi, Enrico Palumbo +2
Generative retrieval for search and recommendation is a promising paradigm for retrieving items, offering an alternative to traditional methods that depend on external indexes and…