  Detecting beneficial feature interactions is essential in recommender
systems, and existing approaches achieve this by examining all the possible
feature interactions. However, the cost of examining all the possible
higher-order feature interactions is prohibitive (exponentially growing with
the order increasing). Hence existing approaches only detect limited order
(e.g., combinations of up to four features) beneficial feature interactions,
which may miss beneficial feature interactions with orders higher than the
limitation. In this paper, we propose a hypergraph neural network based model
named HIRS. HIRS is the first work that directly generates beneficial feature
interactions of arbitrary orders and makes recommendation predictions
accordingly. The number of generated feature interactions can be specified to
be much smaller than the number of all the possible interactions and hence, our
model admits a much lower running time. To achieve an effective algorithm, we
exploit three properties of beneficial feature interactions, and propose
deep-infomax-based methods to guide the interaction generation. Our
experimental results show that HIRS outperforms state-of-the-art algorithms by
up to 5% in terms of recommendation accuracy.
