In recent years, DL has developed rapidly, and personalized services are exploring using DL algorithms to improve the performance of the recommendation system. For personalized services, a successful recommendation consists of two parts: attracting users to click the item and users being willing to consume the item. If both tasks need to be predicted at the same time, traditional recommendation systems generally train two independent models. This approach is cumbersome and does not effectively model the relationship between the two subtasks of "click-consumption". Therefore, in order to improve the success rate of recommendation and reduce computational costs, researchers are trying to model multi-task learning. At present, existing multi-task learning models generally adopt hard parameter sharing or soft parameter sharing architecture, but these two architectures each have certain problems. Therefore, in this work, we propose a novel recommendation model based on real recommendation scenarios, Deep Cross network based on RNN for partial parameter sharing (DCRNN). The model has three innovations: 1) It adopts the idea of cross network and uses RNN network to cross-process the features, thereby effectively improves the expressive ability of the model; 2) It innovatively proposes the structure of partial parameter sharing; 3) It can effectively capture the potential correlation between different tasks to optimize the efficiency and methods for learning different tasks.

翻译：近年来，深度学习发展迅速，个性化服务正在探索利用深度学习算法提升推荐系统的性能。对于个性化服务而言，成功的推荐包含两个部分：吸引用户点击物品以及用户愿意消费该物品。若需同时预测这两个任务，传统推荐系统通常训练两个独立模型。这种方法较为繁琐，且无法有效建模“点击-消费”这两个子任务之间的关系。因此，为提高推荐成功率并降低计算成本，研究人员尝试进行多任务学习建模。当前现有的多任务学习模型多采用硬参数共享或软参数共享架构，但这两种架构各自存在一定问题。为此，本研究基于真实推荐场景提出一种新型推荐模型——基于RNN的部分参数共享深度交叉网络（DCRNN）。该模型具有三个创新点：1）采用交叉网络思想，利用RNN网络对特征进行交叉处理，从而有效提升模型表达能力；2）创新性地提出部分参数共享结构；3）能有效捕捉不同任务间的潜在关联，以优化不同任务的学习效率和方法。