IEEE Trans Cybern. 2019 Jan;49(1):83-96. doi: 10.1109/TCYB.2017.2764918. Epub 2017 Nov 7.
Traditional recommender systems suffer from the data sparsity problem. However, user knowledge acquired in one domain can be transferred and exploited in several other relevant domains. In this context, cross-domain recommender systems have been proposed to create a new and effective recommendation paradigm in which to exploit rich data from auxiliary domains to assist recommendations in a target domain. Before knowledge transfer takes place, building reliable and concrete domain correlation is the key ensuring that only relevant knowledge will be transferred. Social tags are used to explicitly link different domains, especially when neither users nor items overlap. However, existing models only exploit a subset of tags that are shared by heterogeneous domains. In this paper, we propose a complete tag-induced cross-domain recommendation (CTagCDR) model, which infers interdomain and intradomain correlations from tagging history and applies the learned structural constraints to regularize joint matrix factorization. Compared to similar models, CTagCDR is able to fully explore knowledge encoded in both shared and domain-specific tags. We demonstrate the performance of our proposed model on three public datasets and compare it with five state-of-the-art single and cross-domain recommendation approaches. The results show that CTagCDR works well in both rating prediction and item recommendation tasks, and can effectively improve recommendation performance.
传统的推荐系统受到数据稀疏性问题的困扰。然而，在一个领域中获得的用户知识可以被转移和利用到其他几个相关领域中。在这种情况下，跨域推荐系统被提出，以创建一种新的有效的推荐范式，在该范式中利用辅助域中的丰富数据来辅助目标域中的推荐。在进行知识转移之前，建立可靠和具体的域相关性是确保只转移相关知识的关键。社会标签用于显式链接不同的域，特别是在用户和项目都不重叠的情况下。然而，现有的模型只利用了异构域共享的标签的子集。在本文中，我们提出了一个完整的标签诱导跨域推荐（CTagCDR）模型，该模型从标记历史中推断出域间和域内相关性，并应用学习到的结构约束来正则化联合矩阵分解。与类似的模型相比，CTagCDR 能够充分挖掘共享标签和特定于域的标签中编码的知识。我们在三个公共数据集上评估了我们提出的模型的性能，并与五个最先进的单域和跨域推荐方法进行了比较。结果表明，CTagCDR 在评分预测和项目推荐任务中表现良好，能够有效提高推荐性能。
