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.

翻译：协同过滤（CF）已成为构建推荐系统最突出的实现策略之一，其核心思想是利用个体的使用模式生成个性化推荐。对于新上线平台而言，CF技术常面临严重的数据稀疏性问题，这极大限制了其性能表现。现有文献提出了多种解决数据稀疏性的方法，其中跨域协同过滤（CDCF）近年来获得了显著关注。为弥补目标域中可用反馈数据的稀缺性，CDCF方法利用其他辅助域中的可用信息。传统CDCF方法主要致力于寻找跨域实体（用户或项目）的共同集合，并将其作为知识传递的桥梁。然而多数真实世界数据集来源于不同领域，因此常缺乏实体对齐所需的锚点信息或参考信息。本文提出一种领域自适应技术以对齐跨域实体的嵌入表示。我们的方法首先利用可用的文本与视觉信息，分别在辅助域和目标域中独立学习每个实体的多视角潜在表示，随后将这些不同表示进行融合以生成对应的统一表示。接着通过固定统一特征作为锚点，训练域分类器学习领域对齐的嵌入表示。在两个公开基准数据集上的实验表明了我们提出方法的有效性。