论文标题
生成负样本以进行顺序推荐
Generating Negative Samples for Sequential Recommendation
论文作者
论文摘要
为了成功推荐(SR)成功,最近的作品着重于设计有效的顺序编码器,融合侧面信息以及挖掘额外的积极自我划分信号。在每个时间步骤中对负面项目进行采样的策略较少探索。由于用户在培训过程中的兴趣和模型更新的动态,因此考虑了从用户的非相互作用项目集中的随机采样项目,这可能是无信息的。结果,该模型将不准确地了解用户对项目的偏好。识别信息性否定性是具有挑战性的,因为内容的负面项目与动态变化的兴趣和模型参数相关(并且抽样过程也应该是有效的)。为此,我们建议为SR(Genni)生成负样本(项目)。根据当前SR模型对项目的学习用户偏好,在每个时间步骤中都采样了负项目。提出了有效的实施,以进一步加速发电过程,从而使其可扩展到大规模推荐任务。在四个公共数据集上进行的广泛实验验证了为SR提供高质量的负样本的重要性,并证明了Genni的有效性和效率。
To make Sequential Recommendation (SR) successful, recent works focus on designing effective sequential encoders, fusing side information, and mining extra positive self-supervision signals. The strategy of sampling negative items at each time step is less explored. Due to the dynamics of users' interests and model updates during training, considering randomly sampled items from a user's non-interacted item set as negatives can be uninformative. As a result, the model will inaccurately learn user preferences toward items. Identifying informative negatives is challenging because informative negative items are tied with both dynamically changed interests and model parameters (and sampling process should also be efficient). To this end, we propose to Generate Negative Samples (items) for SR (GenNi). A negative item is sampled at each time step based on the current SR model's learned user preferences toward items. An efficient implementation is proposed to further accelerate the generation process, making it scalable to large-scale recommendation tasks. Extensive experiments on four public datasets verify the importance of providing high-quality negative samples for SR and demonstrate the effectiveness and efficiency of GenNi.