Cross domain recommendation using dual inductive transfer learning

Cross Domain Recommendations have made a great impact on the field of online services. It helps the service provider of one domain to understand their users from the information of other domains to recommend the correct product to them. Unlike other traditional approaches, our approach finds both way latent relations between users and items considering the interactions between users and items. Traditional model normally avoids considering any implicit information about the user or item for calculating the latent relation between them. But our model Dual Inductive Transfer Learning(DITL) uses all possible user and item features and the ratings provided for recommendations. Our model learns the user’s preferences across the source and target domain and thus preserves the user’s taste across different but related domains. Our model auto-encodes the features in a feature vector to extract the user’s taste and thus transfers this across domains for efficient recommendation. The proposed model is tested on three huge benchmark datasets, the Douban dataset, the Amazon 5-crore dataset for the domain of movies, books and music whereas Tenrec dataset for the domain of articles and videos and it proves to outperform the classical cross-domain recommendation systems.