Cross-domain Recommendation via Deep Domain Adaptation
(a) NDCG
['Method', 'nDCG@1', 'nDCG@10', 'nDCG@50', 'nDCG100']
For I-DSN, at test time, for unobserved items, we can initialize the softmax weights with the hidden representation of the items returned by the autoencoder. Although we tried this approach but it did not yield better results compared to when the weights were not initialized. Therefore, we report the results of I-DSN with the weights of unobserved items not initialized with the hidden representations. Based on the above results, we aim to produce a model that has good ranking performance. To do so, we calculate nDCG@100 on the validation set and choose the model by changing the weight decay parameters and the iteration step. While all three methods show improvement in their result, DSN shows the best average performance compared to the other methods. Thus, by aligning the objective loss with the evaluation metric, DSN can improve its recommendation performance.