  Online recommendation is an essential functionality across a variety of
services, including e-commerce and video streaming, where items to buy, watch,
or read are suggested to users. Justifying recommendations, i.e., explaining
why a user might like the recommended item, has been shown to improve user
satisfaction and persuasiveness of the recommendation. In this paper, we
develop a method for generating post-hoc justifications that can be applied to
the output of any recommendation algorithm. Existing post-hoc methods are often
limited in providing diverse justifications, as they either use only one of
many available types of input data, or rely on the predefined templates. We
address these limitations of earlier approaches by developing J-Recs, a method
for producing concise and diverse justifications. J-Recs is a recommendation
model-agnostic method that generates diverse justifications based on various
types of product and user data (e.g., purchase history and product attributes).
The challenge of jointly processing multiple types of data is addressed by
designing a principled graph-based approach for justification generation. In
addition to theoretical analysis, we present an extensive evaluation on
synthetic and real-world data. Our results show that J-Recs satisfies desirable
properties of justifications, and efficiently produces effective
justifications, matching user preferences up to 20% more accurately than
baselines.
