Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck

Recommender systems have been widely deployed in many real-world applications, but usually suffer from the longstanding user cold-start problem. As a promising way, CrossDomain 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 domainshared 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. With an additional contrastive information regularizer, CDRIB can also capture cross-domain user-user correlations. In this way, those regularizers encourage the representations to encode the domain-shared information, which has the capability to make recommendations in both domains directly. To the best of our knowledge, this paper is the first work to capture the domain-shared information for cold-start users via variational information bottleneck. Empirical experiments illustrate that CDRIB outperforms the state-of-the-art approaches on four realworld cross-domain datasets, demonstrating the effectiveness of adopting the information bottleneck for CDR.

Automated summary

Recommender systems are widely used but often face the user cold-start problem. CrossDomain Recommendation (CDR) has gained attention as a promising solution by transferring user preferences from the source domain to the target domain. Previous CDR approaches mainly use an Embedding and Mapping (EMCDR) idea, but ignore domain interactions. This paper proposes CDRIB, a novel approach that utilizes the information bottleneck principle to encode domain-shared information and improve recommendation performance.