CROSS-DOMAIN RECOMMENDATION MODEL SAMPLE PROCESSING

原文（中文）

In a method, a plurality of recommended items in a target domain is obtained. A first interaction feature of a sample object in a source domain is fused with a second interaction feature of the sample object in the target domain to obtain a fused interaction feature. Similarity scores between the fused interaction feature and each of the plurality of recommended items are determined. A plurality of hard negative samples (HNSs) is filtered from the plurality of recommended items based on the similarity scores. The plurality of HNSs is combined into a candidate recommended item set. A third interaction feature is fused with a fourth interaction feature to obtain a transfer interaction feature. A plurality of real hard negative samples (RHNSs) is filtered from the plurality of HNSs based on similarity scores between the transfer interaction feature and each of the plurality of HNSs.