48 citations · 116 across the 13 of their papers we have counts for
14 papers
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…
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…
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…
On the Impact of Outlier Bias on User Clicks
Fatemeh Sarvi, Ali Vardasbi, Mohammad Aliannejadi +2
User interaction data is an important source of supervision in counterfactual learning to rank (CLTR). Such data suffers from presentation bias. Much work in unbiased learning to r…
Intersection of Parallels as an Early Stopping Criterion
Ali Vardasbi, Maarten de Rijke, Mostafa Dehghani
A common way to avoid overfitting in supervised learning is early stopping, where a held-out set is used for iterative evaluation during training to find a sweet spot in the number…
Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking
Ali Vardasbi, Fatemeh Sarvi, Maarten de Rijke
There are several measures for fairness in ranking, based on different underlying assumptions and perspectives. PL optimization with the REINFORCE algorithm can be used for optimiz…
Cross-Market Product Recommendation
Hamed Bonab, Mohammad Aliannejadi, Ali Vardasbi +2
We study the problem of recommending relevant products to users in relatively resource-scarce markets by leveraging data from similar, richer in resource auxiliary markets. We hypo…
Mixture-Based Correction for Position and Trust Bias in Counterfactual Learning to Rank
Ali Vardasbi, Maarten de Rijke, Ilya Markov
In counterfactual learning to rank (CLTR) user interactions are used as a source of supervision. Since user interactions come with bias, an important focus of research in this fiel…
Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank
Ali Vardasbi, Maarten de Rijke, Ilya Markov
Unbiased CLTR requires click propensities to compensate for the difference between user clicks and true relevance of search results via IPS. Current propensity estimation methods a…
A Behavioral Analysis on the Reselection of Seed Nodes in Independent Cascade Based Influence Maximization
Ali Vardasbi, Heshaam Faili, Masoud Asadpour
Influence maximization serves as the main goal of a variety of social network activities such as viral marketing and campaign advertising. The independent cascade model for the inf…