  Recommender systems have been widely deployed in many real-world
applications, but usually suffer from the long-standing user cold-start
problem. As a promising way, Cross-Domain Recommendation (CDR) has attracted a
surge of interest, which aims to transfer the user preferences observed in the
source domain to make recommendations in the target domain. Previous CDR
approaches mostly achieve the goal by following the Embedding and Mapping
(EMCDR) idea which attempts to learn a mapping function to transfer the
pre-trained user representations (embeddings) from the source domain into the
target domain. However, they pre-train the user/item representations
independently for each domain, ignoring to consider both domain interactions
simultaneously. Therefore, the biased pre-trained representations inevitably
involve the domain-specific information which may lead to negative impact to
transfer information across domains. In this work, we consider a key point of
the CDR task: what information needs to be shared across domains? To achieve
the above idea, this paper utilizes the information bottleneck (IB) principle,
and proposes a novel approach termed as CDRIB to enforce the representations
encoding the domain-shared information. To derive the unbiased representations,
we devise two IB regularizers to model the cross-domain/in-domain user-item
interactions simultaneously and thereby CDRIB could consider both domain
interactions jointly for de-biasing.
