Mar 23, 2024 · We propose a novel approach based on Neuro-Symbolic computing designed for the knowledge transfer task in recommender systems.

One practical technique to mitigate this issue involves transferring information from other domains or tasks to compensate for scarcity in the target domain, ...

Data sparsity is a well-known historical limitation of recommender systems that still impacts the performance of state-of-the-art approaches.

Mitigating Data Sparsity via Neuro-Symbolic Knowledge Transfer. https://doi.org/10.1007/978-3-031-56063-7_15 ·. Journal: Lecture Notes in Computer Science ...

Mitigating Data Sparsity via Neuro-Symbolic Knowledge Transfer Tommaso Carraro, Alessandro Daniele, Fabio Aiolli and Luciano Serafini. LP 11min. GInRec: A ...

Mitigating Data Sparsity via Neuro-Symbolic Knowledge Transfer. T Carraro, A Daniele, F Aiolli, L Serafini. European Conference on Information Retrieval, 226 ...

Mitigating Data Sparsity via Neuro-Symbolic Knowledge Transfer. ECIR (3) 2024: 226-242. [c9]. view. electronic edition via DOI; unpaywalled version; references ...

Following this idea, we propose a novel approach based on Neuro-Symbolic computing designed for the knowledge transfer task in recommender systems. In ...

Mitigating Data Sparsity via Neuro-Symbolic Knowledge Transfer. KaRS@RecSys 2023: 69-79. [c7]. view. electronic edition via DOI · unpaywalled version ...

Sep 26, 2024 · In this paper, we present a knowledge-aware recommendation model based on neuro-symbolic graph embeddings that encode first-order logic rules.