Gotta embed them all! - knowledge-aware recommendations fusing heterogeneous multimodal item embeddings
Year: 2025
Type: article
Abstract: 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.
Institutions: University of Bari Aldo Moro, University of Pisa
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,390
OpenAlex ID: W4415374408