Marco de Gemmis

The integration of Large Language Models into recommender systems has introduced a new paradigm in which models leverage their pre-trained knowledge to generate recommendations. A prevailing assumption is that an LLM’s inherent knowledge is sufficient to support high-quality recommendations across diverse domains. This article challenges that assumption, positing that in specialized domains, recommendation efficacy is limited by the textual nature of an LLM’s knowledge. To address this limitation, we propose REKALM, a comprehensive integration framework for enhancing LLM-based recommenders through knowledge integration. Central to our approach is the extension of established text-conversion techniques to non-traditional data modalities. We utilize knowledge lexicalization , a process that translates heterogeneous data sources into a unified natural language format. This lexicalized corpus is then used in a knowledge-aware instruction-tuning pipeline to explicitly align the LLM’s internal representations with four distinct types of domain-specific information. We conduct experiments across four distinct domains to validate our framework. Our findings provide consistent empirical evidence that while an LLM’s inherent knowledge may suffice for universally familiar domains like movies, recommendation quality in more specialized areas is significantly improved through knowledge integration. The proposed approach demonstrates that augmenting LLMs with lexicalized, domain-specific knowledge is an effective system-level strategy for advancing the next generation of recommender systems.

The evaluation of Conversational Recommender Systems necessitates robust protocols to measure utility and user satisfaction. While human-in-the-loop testing remains the gold standard, scalability and reproducibility constraints have driven the field toward User Simulators. However, current simulation paradigms predominantly utilize rigid templates or closed-source Large Language Models that exhibit idealized behaviors. These approaches fail to capture user ambiguity, resulting in benchmarks that overestimate system proficiency by assuming crystallized user intent. To address this limitation, we introduce a family of open-weight user simulation models capable of generalizing across diverse e-commerce domains. Leveraging Teacher-Student distillation, we operationalize three distinct behavioral stereotypes: Direct , Vague-Proactive , and Vague-Reactive . Our evaluation of state-of-the-art Agentic Generative Conversational Recommender Systems reveals a critical Robustness Gap : while agents perform proficiently with decisive users, performance collapses when facing passivity and ambiguity. These findings underscore the necessity of our scalable framework for rigorously stress-testing the next generation of conversational agents against realistic, non-cooperative user behaviors.

In this paper, we present a methodology to provide users with knowledge-aware recommendations based on the fusion of multimodal item embeddings. Our approach relies on the intuition that each modality ( i.e., graph, text, video, images, etc.) emphasizes different characteristics and nuances of the items, so it is necessary that a comprehensive knowledge-aware recommender system (KARS) encodes and exploits all the different data sources that are available in a specific domain. Accordingly, we design a multimodal KARS architecture based on a deep neural network that: (a) learns a representation of each uni-modal feature ( i.e., description, trailers, covers, audio signals, and so on) through an appropriate encoder; (b) exploits self-attention and cross-attention to fuse the different sources and refine the embeddings; (c) returns a prediction score which represents user’s interest in the item, which is finally used to generate a top-k recommendation list. In the evaluation, we carried out experiments against two datasets, and the results showed that our approach overcame several baselines for multimodal and knowledge-aware recommendations, thus confirming the intuitions behind this work.