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SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation by Zhang, Chi; Han, Qilong; Chen, Rui; Zhao, Xiangyu; Tang, Peng; Song, Hongtao is a scholarly article available to read on EtoBox.

What is SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation about?

Traditional sequential recommendation methods assume that users' sequence data is clean enough to learn accurate sequence representations to reflect user preferences. In practice, users' sequences inevitably contain noise (e.g., accidental interactions), leading to incorrect reflections of user preferences. Consequently, some pioneer studies have explored modeling sequentiality and correlations in sequences to implicitly or explicitly reduce noise's influence. However, relying on only available intra-sequence information (i.e., sequentiality and correlations in a sequence) is insufficient and may result in over-denoising and under-denoising problems (OUPs), especially for short sequences. To improve reliability, we propose to augment sequences by inserting items before denoising. However, due to the data sparsity issue and computational costs, it is challenging to select proper items from the entire item universe to insert into proper positions in a target sequence. Motivated by the above observation, we propose a novel framework--Self-augmented Sequence Denoising for sequential Recommendation (SSDRec) with a three-stage learning paradigm to solve the above challenges. In the first

Author

Zhang, Chi; Han, Qilong; Chen, Rui; Zhao, Xiangyu; Tang, Peng; Song, Hongtao

Published

2024

Language

EN