Sentiment-guided Sequential Recommendation

Lin Zheng, Naicheng Guo, Weihao Chen, Jin Yu, Dazhi Jiang
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引用次数: 20

Abstract

The existing sequential recommendation methods focus on modeling the temporal relationships of user behaviors and are good at using additional item information to improve performance. However, these methods rarely consider the influences of users' sequential subjective sentiments on their behaviors---and sometimes the temporal changes in human sentiment patterns plays a decisive role in users' final preferences. To investigate the influence of temporal sentiments on user preferences, we propose generating preferences by guiding user behavior through sequential sentiments. Specifically, we design a dual-channel fusion mechanism. The main channel consists of sentiment-guided attention to match and guide sequential user behavior, and the secondary channel consists of sparse sentiment attention to assist in preference generation. In the experiments, we demonstrate the effectiveness of these two sentiment modeling mechanisms through ablation studies. Our approach outperforms current state-of-the-art sequential recommendation methods that incorporate sentiment factors.
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情绪导向的顺序推荐
现有的顺序推荐方法侧重于对用户行为的时间关系进行建模,并善于利用附加的项目信息来提高性能。然而,这些方法很少考虑用户连续主观情绪对其行为的影响——有时人类情绪模式的时间变化对用户的最终偏好起决定性作用。为了研究时间情绪对用户偏好的影响,我们提出通过顺序情绪引导用户行为来生成偏好。具体来说,我们设计了一个双通道融合机制。主通道由情感引导的注意组成,用于匹配和引导顺序用户行为,次通道由稀疏的情感注意组成,以帮助偏好生成。在实验中,我们通过消融研究证明了这两种情感建模机制的有效性。我们的方法优于目前最先进的包含情绪因素的顺序推荐方法。
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