1 citations · 2 across the 17 of their papers we have counts for
6 papers · 1 filter
Robust Fusion of Semantic and Behavioural Signals for LLM Reranking in Personalised Search
Aleksandr V. Petrov, Nathan Stein, Erik Lybecker +4
Personalised search must satisfy query intent while incorporating user context and historical interactions. LLM-based cross-encoders provide a single reranking interface, but injec…
Bootstrapping Conversational Recommendation Agents At Spotify: Synthetic Data Generation and Self-Improvement Loops
Enrico Palumbo, Alexandre Tamborrino, Victor Ode +15
Conversational recommendation agents are a new paradigm for content discovery, enabling users to express complex intents through natural language (e.g., "recommend Italian indie ar…
The Disconnect Between Better Descriptive Reasoning Trace Quality and Recommendation Effectiveness
Gustavo Penha, Juan Elenter, Claudia Hauff +3
Recent work has focused on improving explicit natural-language descriptive reasoning traces for generative recommendation. This includes systems that augment semantic ID (SID) pred…
Do Sequential Recommendation Benchmarks Really Require Higher-Order Sequence Modelling?
Aleksandr V. Petrov, Praveen Chandar, Paul N. Bennett +2
Sequential recommenders increasingly use language-model architectures designed to capture complex, context-dependent interactions. Yet it remains unclear whether widely used benchm…
Hypothesis-Driven Shelf Generation for Personalised Recommendation
Aleksandr V. Petrov, Tarun Chillara, Matthew D. Moellman +13
Modern recommendation interfaces organise content into shelves: themed rows such as "More of What You Like" or "New Releases for You." In production systems, these shelves are typi…
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…