48 citations · 115 across the 12 of their papers we have counts for
10 papers · 1 filter
As It Was: Aligning LLM Search Evaluation with Historical User Preferences
Ali Vardasbi, Gustavo Penha, Enrico Palumbo +3
Large-scale search systems evolve faster than human quality assurance can scale, especially for long-tail intents and multilingual queries. LLM-as-a-judge approaches provide a scal…
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…
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…
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…
The Impact of Group Membership Bias on the Quality and Fairness of Exposure in Ranking
Ali Vardasbi, Maarten de Rijke, Fernando Diaz +1
When learning to rank from user interactions, search and recommender systems must address biases in user behavior to provide a high-quality ranking. One type of bias that has recen…
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank
Shashank Gupta, Philipp Hager, Jin Huang +2
Since its inception, the field of unbiased learning to rank (ULTR) has remained very active and has seen several impactful advancements in recent years. This tutorial provides both…