Recent recommender systems started to use rating elicitation, which asks new users to rate a small seed itemset for inferring their preferences, to improve the quality of initial recommendations. The key challenge of the rating elicitation is to choose the seed items which can best infer the new users' preference. This paper proposes a novel end-to-end Deep learning framework for Rating Elicitation (DRE), that chooses all the seed items at a time with consideration of the non-linear interactions. To this end, it first defines categorical distributions to sample seed items from the entire itemset, then it trains both the categorical distributions and a neural reconstruction network to infer users' preferences on the remaining items from CF information of the sampled seed items. Through the end-to-end training, the categorical distributions are learned to select the most representative seed items while reflecting the complex non-linear interactions. Experimental results show that DRE outperforms the state-of-the-art approaches in the recommendation quality by accurately inferring the new users' preferences and its seed itemset better represents the latent space than the seed itemset obtained by the other methods.

翻译：近期推荐系统开始采用评分引导技术，即要求新用户对一小部分种子项目进行评分以推断其偏好，从而提升初始推荐质量。评分引导的核心挑战在于选择能够最优推断新用户偏好的种子项目。本文提出一种新型端到端深度学习评分引导框架（DRE），该框架在考虑非线性交互的同时一次性选择所有种子项目。为此，框架首先定义类别分布以从整个项目集合中采样种子项目，随后联合训练类别分布与神经重构网络，通过采样种子项目的协同过滤信息推断用户对剩余项目的偏好。通过端到端训练，类别分布能够学习选取最具代表性的种子项目，同时反映复杂的非线性交互。实验结果表明，DRE通过精确推断新用户偏好而优于现有最佳推荐方法，且其种子项目集较其他方法所得集合更能表征潜在空间。