  We present a framework to generate and evaluate thematic recommendations
based on multilayer network representations of knowledge graphs (KGs). In this
representation, each layer encodes a different type of relationship in the KG,
and directed interlayer couplings connect the same entity in different roles.
The relative importance of different types of connections is captured by an
intuitive salience matrix that can be estimated from data, tuned to incorporate
domain knowledge, address different use cases, or respect business logic.
  We apply an adaptation of the personalised PageRank algorithm to multilayer
models of KGs to generate item-item recommendations. These recommendations
reflect the knowledge we hold about the content and are suitable for thematic
and/or cold-start recommendation settings. Evaluating thematic recommendations
from user data presents unique challenges that we address by developing a
method to evaluate recommendations relying on user-item ratings, yet respecting
their thematic nature. We also show that the salience matrix can be estimated
from user data. We demonstrate the utility of our methods by significantly
improving consumption metrics in an AB test where collaborative filtering
delivered subpar performance. We also apply our approach to movie
recommendation using publicly-available data to ensure the reproducibility of
our results. We demonstrate that our approach outperforms existing thematic
recommendation methods and is even competitive with collaborative filtering
approaches.
