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Explainable Recommendation aims at not only providing the recommended items to users, but also making users aware why these items are recommended. Too many interactive factors between users and items can be used to interpret the recommendation in a heterogeneous information network. However, these interactive factors are usually massive, implicit and noisy. The existing recommendation explanation approaches only consider the single explanation style, such as aspect-level or review-level. Todoi:10.1609/aaai.v34i04.6095 fatcat:mb636x2jbbhgxhxjyzxihj53w4