Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback. RS models evaluated on such datasets often lack practical values for large-scale real-world applications. In this paper, we describe Tenrec, a novel and publicly available data collection for RS that records various user feedback from four different recommendation scenarios. To be specific, Tenrec has the following five characteristics: (1) it is large-scale, containing around 5 million users and 140 million interactions; (2) it has not only positive user feedback, but also true negative feedback (vs. one-class recommendation); (3) it contains overlapped users and items across four different scenarios; (4) it contains various types of user positive feedback, in forms of clicks, likes, shares, and follows, etc; (5) it contains additional features beyond the user IDs and item IDs. We verify Tenrec on ten diverse recommendation tasks by running several classical baseline models per task. Tenrec has the potential to become a useful benchmark dataset for a majority of popular recommendation tasks.

翻译：现有的推荐系统基准数据集要么规模较小，要么用户反馈形式极为有限。基于此类数据集评估的推荐模型往往缺乏大规模实际应用场景的实用价值。本文介绍Tenrec——一个新颖且公开可用的推荐系统数据集，它记录了四种不同推荐场景下的多种用户反馈。具体而言，Tenrec具有以下五个特征：（1）大规模：包含约500万用户和1.4亿次交互；（2）不仅包含正向用户反馈，还包含真实负反馈（区别于单类推荐）；（3）在四种不同场景中存在重叠的用户和物品；（4）包含点击、点赞、分享、关注等多种形式的用户正向反馈；（5）除用户ID和物品ID外，还包含额外特征。我们通过在十个不同的推荐任务上运行若干经典基线模型来验证Tenrec。Tenrec有望成为大多数主流推荐任务的有用基准数据集。