Explanation Chains: Recommendations by Explanation

Given a set of candidate items, Recommendation by Explanation constructs a justification for recommending each item, in the form of what we call an Explanation Chain, and then recommends those candidates that have the best explanations. By unifying recommendation and explanation, this approach enables us to find relevant recommendations with explanations that have a high degree of both fidelity and interpretability. Experimental results on a movie recommendation dataset show that our approach also provides sets of recommendations that have a high degree of serendipity, low popularity-bias and high diversity.