Explainable cross-domain recommendations through relational learning

Abstract

We propose a method to generate explainable recommendation rules on cross-domain problems. Our two main contributions are: i) using relational learning to generate the rules which are able to explain clearly why the items were recommended to the particular user, ii) using the user's preferences of items on different domains and item attributes to generate novel or unexpected recommendations for the user. To illustrate that our method is indeed feasible and applicable, we conducted experiments on music and movie domains. Recommender systems (RS) currently become one of the most basic supportive techniques in an online landscape/world. RS has proven to be a major source of enhanced functionality, user satisfaction, and revenue improvement. The most common critical issues found with RS include maximizing prediction accuracy, solving cold-start problem, reducing sparsity, providing novelty, diversity and serendipity. However, solving one problem may create another problem, or a trade-off. All issues have not been perfectly solved since many current recommender algorithms seem to be locked away inside a black box. Once an algorithm is processed, it is quite difficult to understand why it gives a particular recommendation to a set of data inputs. If we can understand the reason behind the recommendation, we believe we will be able to possibly find the way to handle such problems more effectively. Cross-domain approach improves prediction accuracy by reducing data sparsity and offering added values to recommendations by providing diversity, novelty and serendipity predictions (Cantador et al. 2015). In cross-domain recommendation tasks, the systems recommend items in the target domain to users in the source domain. There are two types of cross-domain approach: Aggregating and Transferring and they are different mainly based on how knowledge from the source domain is exploited. Relational learning has already shown its use in RS. The evidences from the researches by Kouki et al. (2015) and Catherine and Cohen (2016) indicate that relational learning provides a better recommendation performance by incorporating additional information compared to traditional methods with a single dyadic relationship between the objects, i.e. users and items. Hence, the relational learning captured our interest to model and provide a potential solution for explainable cross-domain recommendations. We propose a method to generate explainable recommendation rules on cross-domain problems using relational learning. The generated rules explain why the system gives a particular recommendation to a user. The rules are simple and understandable. Moreover, some novel recommendations in the primary domain are generated based on the user's preference on additional domains.