Retracing and Restoring: Chronological Context Preservation for Effective Dynamic Recommendation

Dynamic recommender systems (DRSs) model time-varying user preferences and item popularity by sequentially updating low-dimensional representations (i.e., embeddings) of users and items upon each interaction. However, existing approaches often fail to preserve the chronological context of past interactions, overwriting historical signals with the current context and thereby misrepresenting user preferences and item attributes. To address this limitation, we propose a novel DRS framework, named TraceRec, which (1) retraces historical interaction paths and (2) restores chronological context for effective dynamic recommendation. Specifically, TraceRec retraces historical interaction paths via reverse chronological random walks and restores the embeddings of neighbors along these paths to their interaction-time context using a past-projection mechanism. Extensive experiments on six real-world dynamic recommendation datasets demonstrate that TraceRec consistently and significantly outperforms eight competitors, achieving up to 17.05% improvement in Recall@10. The code is available at https://github.com/kmj0792/TraceRec.