KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos

TL;DR

KuaiRand is a large-scale, unbiased sequential recommendation dataset from Kuaishou, capturing diverse user feedback on randomly exposed videos, enabling research on debiasing and advanced recommendation models.

Contribution

This work introduces KuaiRand, a comprehensive dataset with rich user and item features, capturing multiple feedback signals on randomly exposed videos, addressing limitations of existing datasets.

Findings

Contains millions of interactions with diverse feedback signals

Includes rich user and item features for modeling

Supports research on debiasing and advanced recommendation techniques

Abstract

Recommender systems deployed in real-world applications can have inherent exposure bias, which leads to the biased logged data plaguing the researchers. A fundamental way to address this thorny problem is to collect users' interactions on randomly expose items, i.e., the missing-at-random data. A few works have asked certain users to rate or select randomly recommended items, e.g., Yahoo!, Coat, and OpenBandit. However, these datasets are either too small in size or lack key information, such as unique user ID or the features of users/items. In this work, we present KuaiRand, an unbiased sequential recommendation dataset containing millions of intervened interactions on randomly exposed videos, collected from the video-sharing mobile App, Kuaishou. Different from existing datasets, KuaiRand records 12 kinds of user feedback signals (e.g., click, like, and view time) on randomly exposed videos inserted in the recommendation feeds in two weeks. To facilitate model learning, we further collect rich features of users and items as well as users' behavior history. By releasing this dataset, we enable the research of advanced debiasing large-scale recommendation scenarios for the first time. Also, with its distinctive features, KuaiRand can support various other research directions such as interactive recommendation, long sequential behavior modeling, and multi-task learning. The dataset and its news will be available at https://kuairand.com.