Recommender systems are prone to be misled by biases in the data. Models trained with biased data fail to capture the real interests of users, thus it is critical to alleviate the impact of bias to achieve unbiased recommendation. In this work, we focus on an essential bias in micro-video recommendation, duration bias. Specifically, existing micro-video recommender systems usually consider watch time as the most critical metric, which measures how long a user watches a video. Since videos with longer duration tend to have longer watch time, there exists a kind of duration bias, making longer videos tend to be recommended more against short videos. In this paper, we empirically show that commonly-used metrics are vulnerable to duration bias, making them NOT suitable for evaluating micro-video recommendation. To address it, we further propose an unbiased evaluation metric, called WTG (short for Watch Time Gain). Empirical results reveal that WTG can alleviate duration bias and better measure recommendation performance. Moreover, we design a simple yet effective model named DVR (short for Debiased Video Recommendation) that can provide unbiased recommendation of micro-videos with varying duration, and learn unbiased user preferences via adversarial learning. Extensive experiments based on two real-world datasets demonstrate that DVR successfully eliminates duration bias and significantly improves recommendation performance with over 30% relative progress. Codes and datasets are released at https://github.com/tsinghua-fib-lab/WTG-DVR.
翻译:建议系统容易被数据中的偏差误导。 受有偏差数据培训的模型无法捕捉用户的真正利益,因此,减少偏差的影响对于实现公正建议至关重要。 在这项工作中,我们侧重于微视建议中的基本偏差,即时间偏差。 具体地说,现有的微视建议系统通常将观察时间视为最关键的衡量标准,以衡量用户观看视频的时间长短。 由于时间较长的视频往往有较长的监视时间,存在某种时间偏差,使长的视频往往比短视频更容易被推荐。 在本文中,我们从经验上表明,常用的衡量标准很容易受到时间偏差的影响,因此,它们不适合评估微视建议。为了解决这个问题,我们进一步提出了一个公正的评价指标,称为WTG(观察时间增益的短) 。 经验性结果显示,WTG可以减轻时间偏差,更好地衡量建议性能。 此外,我们设计了一个简单有效的模型,名为DVVR(对易分数/偏差视频建议来说是短的),可以提供不偏倚的微视像片建议建议,其持续时间长短不偏差,使它们不适合时间差的尺度不近,使它们无法评价。 我们进一步学习D- 30的用户偏向性用户偏向,通过对抗学习,在D- R- 成功学习学习,在D- bromabalal- slab- slaxxx