A new CDR framework leverages both positive and negative feedback, improving both convergence speed and final accuracy compared to existing models (IMAGE)
Caption
Deep User Preference Gating Transfer for Cross-Domain Recommendation (DUPGT-CDR), a newly developed CDR model, extracts high- and low-rating interaction vectors from the source domain, generates corresponding transformation vectors, and adaptively fuses them via a gating network. This allows the framework to achieve lower prediction errors than existing models and can aid in building highly integrated personalization in commerce, entertainment, and education.
Credit
Associate Professor Keiko Ono from Doshisha University, Japan
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CC BY-NC-ND