Conversational Recommender Systems (CRS) have recently drawn attention due to their capacity of delivering personalized recommendations through multi-turn natural language interactions. In this paper, we fit into this research line and we introduce a ...

In this paper, we introduce a Knowledge-aware Recommender System (KARS) based on Graph Neural Networks that exploit pre-trained content-based embeddings to improve the representation of users and items. Our approach relies on the intuition that textual ...

The last few years showed a growing interest in the design and development of Knowledge-Aware Recommender Systems (KARSs). This is mainly due to their capability in encoding and exploiting several data sources, both structured (such as knowledge graphs) ...

In this paper, we present a comparative analysis of the trade-off between the performance of state-of-the-art recommendation algorithms and their environmental impact. In particular, we compared 18 popular recommendation algorithms in terms of both ...

In the last few years, Knowledge-Aware Recommender Systems (KARSs) got an increasing interest in the community thanks to their ability at encoding diverse and heterogeneous data sources, both structured (such as knowledge graphs) and unstructured (such ...

In this paper, we present a strategy to provide users with knowledge-aware recommendations based on the combination of graph neural networks and sentence encoders. In particular, our approach relies on the intuition that different data sources (i.e., ...

In this paper, we present a knowledge-aware recommendation framework based on neuro-symbolic graph embeddings that encode first-order logical (FOL) rules. In particular, our workflow starts from a knowledge graph (KG) encoding user preferences (based on ...