Semantic-Guided Data Augmentation and Filtering for Sequential Recommendation

Data augmentation is widely employed in sequential recommendation to mitigate data sparsity issues without requiring additional supervised labels. This process generally consists of two stages: augmented data construction and augmented data filtering. Although existing methods have investigated various effective strategies for both stages, they still encounter two primary limitations: (1) limited semantic diversity during the augmented data construction stage, and (2) inefficient filtering mechanisms during the augmented data filtering stage. To address these limitations and achieve more effective data augmentation, this paper proposes Semantic-guided Data Augmentation and Filtering for Sequential Recommendation (SDARec), which leverages semantic information of items to guide both the construction and filtering of augmented data. In the augmented data construction stage, dataset retrieval augmentation and sequence retrieval augmentation strategies are designed to construct semantically controllable and diverse augmented data. In the augmented data filtering stage, a large language model is employed to evaluate and filter the augmented data through a chain-of-thought prompting approach, eliminating the need for expert knowledge or complex heuristic designs. SDARec performs augmentation at the dataset level without requiring model training, making it highly generalizable and adaptable to various conventional recommendation models. Extensive experiments on three public datasets demonstrate the effectiveness of SDARec. Source code is available at https://github.com/zzzzzdev/SDARec .