Parallel Inductive Shift Learning Based Recommendation System

In today’s world, online services have revolutionized human activities and thus the consumers expect their service providers to make their online experiences more fruitful by recommending the relevant services to them. In this case, it becomes really challenging for the service providers to provide recommendation to a user whose information’s and preferences are unavailable. This issue is handled by cross-domain approach, which explores similar users across various domains in the same platform. However, the main concern with this cross-domain approach is that the information needs to be available in any domain of one platform. Thus, a multidomain recommendation is designed to optimize the recommendation system performance by analyzing the information obtained from multiple platforms. However, existing multidomain recommendation model has mainly two challenges. First, there are no overlapping users to understand the similarities between them. Second, the transfer learning approach in multidomain allows the transfer of information from only the source to the target domain. Therefore, our proposed approach consider the parallel inductive shift learning (PISL) model to address these two above-mentioned challenges. For the first challenge, we have focused to identify the similarities between user–user and user–item by considering various features of user and item. For the next challenge, our proposed model analyzes the source and the target domain simultaneously and thus does a parallel transfer of information from the source to the target domain and vice versa. We have tested our model for three real-life movie and book datasets i.e. for the movie dataset we have used Movielens, Amazon, and Netflix datasets. In contrast, for the book dataset, we have used the Amazon, Good Reads, and Book Crossing dataset, which proves to outperform the other state-of-the-art approaches.