1 citations · 2 across the 17 of their papers we have counts for
8 papers · 1 filter
From IR to RecSys: Evaluating LLM-based Judges in Cranfield-style Recommendation Collections
Gustavo Penha, Aleksandr V. Petrov, Claudia Hauff +9
The Cranfield paradigm has long provided reliable, reproducible evaluation in ad hoc retrieval, and recent work has begun extending this framework to recommender systems. A recent…
Stream: Scaling up Mechanistic Interpretability to Long Context in LLMs via Sparse Attention
J Rosser, José Luis Redondo García, Gustavo Penha +2
As Large Language Models (LLMs) scale to million-token contexts, traditional Mechanistic Interpretability techniques for analyzing attention scale quadratically with context length…
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…
Adaptive Repetition for Mitigating Position Bias in LLM-Based Ranking
Ali Vardasbi, Gustavo Penha, Claudia Hauff +1
When using LLMs to rank items based on given criteria, or evaluate answers, the order of candidate items can influence the model's final decision. This sensitivity to item position…
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…