As users often express their preferences with binary behavior data~(implicit
feedback), such as clicking items or buying products, implicit feedback based
Collaborative Filtering~(CF) models predict the top ranked items a user might
like by leveraging implicit user-item interaction data. For each user, the
implicit feedback is divided into two sets: an observed item set with limited
observed behaviors, and a large unobserved item set that is mixed with negative
item behaviors and unknown behaviors. Given any user preference prediction
model, researchers either designed ranking based optimization goals or relied
on negative item mining techniques for better optimization. Despite the
performance gain of these implicit feedback based models, the recommendation
results are still far from satisfactory due to the sparsity of the observed
item set for each user. To this end, in this paper, we explore the unique
characteristics of the implicit feedback and propose Set2setRank framework for
recommendation. The optimization criteria of Set2setRank are two folds: First,
we design an item to an item set comparison that encourages each observed item
from the sampled observed set is ranked higher than any unobserved item from
the sampled unobserved set. Second, we model set level comparison that
encourages a margin between the distance summarized from the observed item set
and the most "hard" unobserved item from the sampled negative set. Further, an
adaptive sampling technique is designed to implement these two goals. We have
to note that our proposed framework is model-agnostic and can be easily applied
to most recommendation prediction approaches, and is time efficient in
practice. Finally, extensive experiments on three real-world datasets
demonstrate the superiority of our proposed approach.

研究人员在探索隐性反馈的独特特征之后，提出了 Set2setRank 框架来解决由于用户的行为数据缺乏导致的推荐结果不尽如人意的问题。该框架的优化目标是通过对比已观察到的和未观察到的商品，最大化满足用户偏好的商品排名。