Cross-domain Recommendation via Deep Domain Adaptation
Table 1. The data statitics for CdMF
['[EMPTY]', 'News', 'Movie']
Baseline Description In our experiments, we compare our method with the following baselines. Most Popular Items (POP) : This suggests most popular items in the training data. The comparison of our method with this shows how well our method achieves personalization. Cross-domain Matrix Factorization (CdMF): This is a state-of-the-art collaborative filtering method for cross-domain recommendation. By scraping the same user logs we used for our approach, we obtain a training dataset. We eliminte users with history logs fewer than 5 for the video data and 20 for the news data. We construct a user-item matrix for each domain, with the value of observed entries 1 and of unobserved entries 0. The unobserved entries are randomly sampled. As with our method, we use the 80 percent of the instances for training and the rest for validation. The dimensionality of latent vectors is set to 10. Although 10 is small, as the training involves the computation of the inverse matrices of this dimensionality for MCMC sampling, it takes very long time even for the dimensionality 10. Also, to make the computation faster, we truncate the iteration at the 700 sample and discard the first 100 samples as burn-in. The hyper parameters Because of the time computational complexity, it is hard to optimize these parameters. Therefore we make these parameters fixed. Neural Network (NN): To investigate the performance of domain adaptation, we also test the neural network without domain adaptation. Parameters for weight decay are chosen from the same parameter grid as our method.