本文认为现有的cross-domain方法用到的side information种类有限。提出了可以同时使用content，review，score三种信息的模型。
模型基本上基于aSDAE组建起来的。两个模型贡献：
（1）利用aSDAE构建review-based的物品表达和content-based的物品表达之间的关系（如，最小化两种表达之间的距离）
（2）利用MLP构建source domain中User vector和target domain中User vector之间的关系，例如，前者作为输入，后者作为输出。这样，在后者表达不准确的前提下（cold-start）可以利用前者估计后者，从而做出推荐。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems | |||||||||||
| 文献作者 | Wenjing Fu | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| content-based; review-based; score-based; SDAE; aSDAE; deep learning; cross-domain | |||||||||||
| 摘要描述 | |||||||||||
| As one promising way to solve the challenging issues of data sparsity and cold start in recommender systems, cross-domain recommendation has gained increasing research interest recently. Cross-domain recommendation aims to improve the recommendation performance by means of transferring explicit or implicit feedback from the auxiliary domain to the target domain. Although the side information of review texts and item contents has been proven to be useful in recommendation, most existing works only use one kind of side information and cannot deeply fuse this side information with ratings. In this paper, we propose a Review and Content based Deep Fusion Model named RC-DFM for cross-domain recommendation. We first extend Stacked Denoising Autoencoders (SDAE) to effectively fuse review texts and item contents with the rating matrix in both auxiliary and target domains. Through this way, the learned latent factors of users and items in both domains preserve more semantic information for recommendation. Then we utilize a multi-layer perceptron to transfer user latent factors between the two domains to address the data sparsity and cold start issues. Experimental results on real datasets demonstrate the superior performance of RC-DFM compared with state-of-the-art recommendation methods. | |||||||||||
