Recently, the reciprocal recommendation, especially for online dating applications, has attracted more and more research attention. Different from conventional recommendation problems, the reciprocal recommendation aims to simultaneously best match users' mutual preferences. Intuitively, the mutual preferences might be affected by a few key attributes that users like or dislike. Meanwhile, the interactions between users' attributes and their key attributes are also important for key attributes selection. Motivated by these observations, in this paper we propose a novel reinforced random convolutional network (RRCN) approach for the reciprocal recommendation task. In particular, we technically propose a novel random CNN component that can randomly convolute non-adjacent features to capture their interaction information and learn feature embeddings of key attributes to make the final recommendation. Moreover, we design a reinforcement learning based strategy to integrate with the random CNN component to select salient attributes to form the candidate set of key attributes. We evaluate the proposed RRCN against a number of both baselines and the state-of-the-art approaches on two real-world datasets, and the promising results have demonstrated the superiority of RRCN against the compared approaches in terms of a number of evaluation criteria.

翻译：最近,对等建议,特别是在线约会应用程序的对等建议吸引了越来越多的研究关注。与传统的建议问题不同,对等建议旨在同时与用户的相互偏好相匹配。直观地说,相互偏好可能受到用户喜欢或不喜欢的几个关键属性的影响。与此同时,用户属性及其关键属性之间的相互作用对于关键属性的选择也很重要。受这些观察的启发,我们在本文件中为对等建议任务提出了一个新颖的强化随机随机网络(RRCN)方法。特别是,我们从技术上提出了一个新的随机随机随机随机的CNN组件,它可以随机调用非对称特征来捕捉他们的互动信息,并学习关键属性的嵌入功能来提出最后建议。此外,我们设计了一个强化学习战略,与随机CNN组件结合,以选择显著属性形成候选的关键属性组合。我们根据一些基线和两个真实世界数据集的最新方法对拟议的RRCCN进行了评估。有希望的结果表明RCN优于一些评价标准的比较方法。