Abstract
Session-based recommendations which aim to predict subsequent user–item interactions based on historical user behaviour during anonymous sessions can be challenging to carry out. Two main challenges need to be addressed and improved: (1) how does one analyze these sessions to accurately and completely capture users’ preferences, and (2) how does one identify and eliminate any interference caused by noisy behavior? Existing methods have not adequately addressed these issues since they either neglect the valuable insights that can be gained from analyzing consecutive groups of items or fail to take these noisy data in sessions seriously and handle them properly, which can jointly impede recommendation systems from capturing users’ real intentions. To address these two problems, we designed a multi-order semantic denoising (MSD) model for session-based recommendations. Specifically, we grouped items of different lengths into varying multi-order semantic units to mine the user’s primary intentions from multiple dimensions. Meanwhile, a novel denoising network was designed to alleviate the interference of noisy behavior and provide a more precise session representation. The results of extensive experiments on three real-world datasets demonstrated that the proposed MSD model exhibited improved performance compared with existing state-of-the-art methods in session-based recommendations.
Published Version
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