论文标题
明星：基于会话的时间吸引推荐系统
STAR: A Session-Based Time-Aware Recommender System
论文作者
论文摘要
基于会话的推荐人（SBRS）旨在预测用户对他们以前的会话互动的下一个偏好，而没有关于它们的历史信息。现代SBR利用深层神经网络在持续的会话中将用户当前的兴趣映射到潜在空间，以便可以预测他们的下一个偏好。尽管最先进的SBR模型取得了令人满意的结果，但大多数人专注于研究会议内部事件的顺序，同时忽略这些事件的时间细节。在本文中，我们研究了会话时间信息在增强SBR的性能方面的潜力，可以想象，可以通过反映匿名用户的瞬时利益或他们在会话期间的心态变化。我们提出了“星际框架”，该框架利用会话内事件之间的时间间隔来构建项目和会话的更有信息的表示形式。我们的机制通过嵌入时间间隔不使用离散化来修改会话表示。关于YooChoose和Diginetica数据集的经验结果表明，建议的方法在回忆和MRR标准中优于最先进的基线模型。
Session-Based Recommenders (SBRs) aim to predict users' next preferences regard to their previous interactions in sessions while there is no historical information about them. Modern SBRs utilize deep neural networks to map users' current interest(s) during an ongoing session to a latent space so that their next preference can be predicted. Although state-of-art SBR models achieve satisfactory results, most focus on studying the sequence of events inside sessions while ignoring temporal details of those events. In this paper, we examine the potential of session temporal information in enhancing the performance of SBRs, conceivably by reflecting the momentary interests of anonymous users or their mindset shifts during sessions. We propose the STAR framework, which utilizes the time intervals between events within sessions to construct more informative representations for items and sessions. Our mechanism revises session representation by embedding time intervals without employing discretization. Empirical results on Yoochoose and Diginetica datasets show that the suggested method outperforms the state-of-the-art baseline models in Recall and MRR criteria.
