Incorporating Forgetting Curve and Memory Replay for Evolving Socially-aware Recommendation

Abstract

Social recommendations play a crucial role in helping users filter information and discover potential requirements. However, existing works often ignore the effects of memory patterns and social inconsistency, which hide the recommendation for capturing evolving user interests. To overcome these problems, a model incorporating the Forgetting curve and Memory Replay for Evolving Socially-aware recommendation (FMRES) is proposed to navigate users’ fresh interests. Specifically, a cognitive-inspired Ebbinghaus curve is integrated with item attributes to consider users’ personalized interest forgetting and retention. Then, the memory replay mechanism is employed to revive forgotten yet valuable items, fostering user engagement and enhancing relevance in recommendations. By aggregating the neighbors’ social characters, consistent friends are sampled to identify meaningful and impactful relationships. Finally, temporal representations of users and items are incorporated to track the evolution of users’ interests by utilizing gated recurrent units. Extensive experiments on three datasets demonstrate that the proposed model consistently outperforms advanced baseline methods over various metrics.