Knowledge-Aware Recommender Systems based on Multi-Modal Information Sources
Pages 1312 - 1317
Abstract
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) and unstructured (such as plain text). Nowadays, a lot of models at the state-of-the-art in KARSs use deep learning, enabling them to exploit large amounts of information, including knowledge graphs (KGs), user reviews, plain text, and multimedia content (pictures, audio, videos). In my Ph.D. I will follow this research trend and I will explore and study techniques for designing KARSs leveraging representations learnt from multi-modal information sources, in order to provide users with fair, accurate, and explainable recommendations.
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Index terms have been assigned to the content through auto-classification.
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Published In
September 2023
1406 pages
ISBN:9798400702419
DOI:10.1145/3604915
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- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
- SIGecom: Special Interest Group on Economics and Computation
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Published: 14 September 2023
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RecSys '23: Seventeenth ACM Conference on Recommender Systems
September 18 - 22, 2023
Singapore, Singapore
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Overall Acceptance Rate 254 of 1,295 submissions, 20%
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