An Explainable Recommendation Based on Acyclic Paths in an Edge-Colored Graph

Abstract

We propose a novel recommendation algorithm based on acyclic paths in an edge-colored graph. In our method, all the objects including users, items to recommend, and other things usable to recommendation are represented as vertices in an edge-colored directed graph, in which edge color represents relation between the objects of its both ends. By setting each edge weight appropriately so as to reflect how much the object corresponding to its one end is preferred by people who prefer the object corresponding to its other end, the probability of an s-t path, which is defined as the product of its component edges’ weights, can be regarded as preference degree of item t (item corresponding to vertex t) by user s (user corresponding to vertex s) in the context represented by the path. Given probability threshold $\theta$, the proposed algorithm recommends user s to item t that has high sum of the probabilities of all the acyclic s-t paths whose probability is at least $\theta$. For item t recommended to user s, the algorithm also shows high probability color sequences of those s-t paths, from which we can know main contexts of the recommendation of item t for user s. According to our experiments using real-world datasets, the recommendation performance of our method is comparable to the non-explainable state-of-the-art recommendation methods.