Marta Moscati,
"Multimodal Representation Learning for high-qualityRecommendations in Cold-start and Beyond-Accuracy"
: Proceedings of the 18th ACM Conference on Recommender Systems(RecSys), 2024, 2024
Original Titel:
Multimodal Representation Learning for high-qualityRecommendations in Cold-start and Beyond-Accuracy
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 18th ACM Conference on Recommender Systems(RecSys), 2024
Original Kurzfassung:
Recommender systems (RS) traditionally leverage the large amount
of user?item interaction data. This exposes RS to a lower recom
mendation quality in cold-start scenarios, as well as to a low rec
ommendation quality in terms of beyond-accuracy evaluation met
rics. State-of-the-art (SotA) models for cold-start scenarios rely on
the use of side information on the items or the users, therefore
relating recommendation to multimodal machine learning (ML).
However, the mostrecent techniques from multimodal MLareoften
not applied to the domain of recommendation. Additionally, the
evaluation of SotA multimodal RS often neglects beyond-accuracy
aspects of recommendation. In this work, we outline research into
designing novel multimodal RS based on SotA multimodal ML ar
chitectures for cold-start recommendation, and their evaluation and
benchmark with preexisting multimodal RS in terms of accuracy
and beyond-accuracy aspects of recommendation quality
Forschungs-