Aug 6, 2023 · What is the accuracy performance of multimodal-aware recommender systems and is it aligned with the findings from the existing literature?

In this paper, we propose one of the first benchmark analyses on the performance of MRSs by comparing five popular and recent approaches on widely-adopted ...

This is the official implementation of the paper Disentangling the Performance Puzzle of Multimodal-aware Recommender Systems, accepted as full paper at ...

Disentangling the Performance Puzzle of Multimodal-aware Recommender Systems. D Malitesta, G Cornacchia, C Pomo, T Di Noia. EvalRS@ KDD, 2023. 7, 2023. On ...

Disentangling the Performance Puzzle of Multimodal-aware Recommender Systems · pdf icon · Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di ...

Disentangling the Performance Puzzle of Multimodal-aware Recommender Systems. D Malitesta, G Cornacchia, C Pomo, T Di Noia. 2nd Workshop on A Well-Rounded ...

This tutorial aims to present and examine three key aspects that characterize GNNs for recommendation: (i) the reproducibility of state-of-the-art approaches.

In this work, we select four popular and recent approaches in multimodal recommendation, namely, VBPR [27], MMGCN [63],. GRCN [62], and LATTICE [66], and test ...

Disentangling the Performance Puzzle of Multimodal-aware Recommender Systems Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia 2nd ...

Oct 8, 2024 · In this work, we outline research into designing novel multimodal RS based on SotA multimodal ML architectures for cold-start recommendation.