  Collaborative Filtering (CF) has emerged as one of the most prominent
implementation strategies for building recommender systems. The key idea is to
exploit the usage patterns of individuals to generate personalized
recommendations. CF techniques, especially for newly launched platforms, often
face a critical issue known as the data sparsity problem, which greatly limits
their performance. Several approaches in the literature have been proposed to
tackle the problem of data sparsity, among which cross-domain collaborative
filtering (CDCF) has gained significant attention in the recent past. In order
to compensate for the scarcity of available feedback in a target domain, the
CDCF approach utilizes information available in other auxiliary domains.
Traditional CDCF approaches primarily focus on finding a common set of entities
(users or items) across the domains, which then act as a conduit for knowledge
transfer. Nevertheless, most real-world datasets are collected from different
domains, so they often lack information about anchor points or reference
information for entity alignment. This paper introduces a domain adaptation
technique to align the embeddings of entities across the two domains. Our
approach first exploits the available textual and visual information to
independently learn a multi-view latent representation for each entity in the
auxiliary and target domains. The different representations of the entity are
then fused to generate the corresponding unified representation. A domain
classifier is then trained to learn the embedding for the domain alignment by
fixing the unified features as the anchor points. Experiments on two publicly
benchmark datasets indicate the effectiveness of our proposed approach.
