KwaiRec | Fully Observed Dataset for Recommender Systems

KwaiRec is a Python library. KwaiRec has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However KwaiRec build file is not available. You can download it from GitHub.

KwaiRec is a real-world dataset collected from the recommendation logs of the video-sharing mobile app Kwai. For now, it is the first dataset that contains a fully observed user-item interaction matrix. For the term "fully observed", we mean there are almost no missing values in the user-item matrix, i.e., each user has viewed each video and leaved feedback. The following figure illustrates the user-item matrices in traditional datasets and our proposed KwaiRec. With all user's preference known, KwaiRec can used in offline evaluation (i.e., offline A/B test) for recommendation models. It can benefit lots of research directions, such as unbiased recommendation, interactive/conversational recommendation, or reinforcement learning (RL) and off-policy evaluation (OPE) for recommendation.