# A Discriminative-Based Geometric Deep Learning Model for Cross Domain Recommender Systems
## 4 Experimental Setup and Results Analysis
### Database Selection and Experiment Evaluation  
To verify the effectiveness of the proposed D-GDL method, the performance of the discriminative geometric deep learning approach for cross-domain recommender systems is evaluated by comparison with the following six baseline approaches: A hinge-loss based codebook transfer for cross-domain recommendation with non-overlapping data (TCH) [2], A cross-domain recommendation approach based on topic modeling and ontology (TMO) [47], personalized transfer of user preferences for cross-domain recommendation (PTUPCDR) [48], cross-domain recommendation to cold-start users via variational information bottleneck (CDRIB) [49], deep learning-based matrix factorization (DLMF) [50] and heterogeneous multidomain recommender system through adversarial learning (HM-Rec) [51]. All the experimental results were obtained by twenty-fold cross-validation. The training samples were randomly selected from various video datasets, and the rest set as test samples. In our experiments, four datasets were utilized from different real-world settings as follows: Flixter, Netflix, CiteULike. These datasets were chosen taking into consideration their different degrees of sparsity and scale to mimic the different practical traits. Flixter is a social network platform, where users can rate movies and users can also add friends or other users to create a social network. Two datasets, namely CiteULike-t and CiteULike-a, were chosen from CiteULike [52]. CiteULike-t was selected independently of CiteULike-a, which is mostly from [53]. The Netflix dataset also consists of movie titles and ratings from Netflix challenge datasets. The detailed statistics of the datasets are as presented in Table 1.  
As in the case of [34], users with fewer than three articles were excluded. It can be seen in Table 1 that CiteULike-t has many more users and items than CiteULike-a. Additionally, CiteULike-t is comparatively sparser as only 0.07% of its user-item matrix entries contain rating compared with 0.22% for CiteULike-a. To maintain consistency with the implicit feedback mechanisms based on the CiteULike datasets, only positive ratings of 5 were extracted for testing and training with the Netflix dataset, and users with fewer than three positive ratings were not considered either. The preprocessing text information, which was the content of the items including the plots of the movies, was extracted from the abstracts and the titles of the articles. The top S discriminative words were selected according to their tf-idf values after eliminating the stop words. The tf-idf values selected to represent the vocabularies S are respectively 8000, 20,000, 20,000 and 15,000 for the four datasets. Again, it can be seen in Table 1 that the user-item-rating matrices of the CiteULike datasets are much sparser than those of Netflix, and that the Netflix matrices are much sparser than those of Flixster. Hence, CiteULike datasets can better evaluate the performance with high sparsity of the proposed model. The datasets used have evolved over the years; however, there is no substantial difference between the content and structure of these datasets. Furthermore, to ascertain the effectiveness of the proposed approach, we also considered cross-domain datasets such as the Amazon and the Imhonet datasets. The Imhonet dataset was sourced from an online Russian recommender site at Imhonet.ru. These give users the opportunity to review and rate a large number of items, some of which are 3D and range across several domains including movies, architectural monuments, mobile phones and books. The dataset also contains some elements of social networks with blogs, friendship networks and comments. It is a unique dataset and is available across many cross-domain recommendations because it gives explicit user feedback (ratings) with varied domains. The Imhonet dataset considered against this research contains movies, books, games and perfumes and it consists of the complete set of user ratings across the four domains.