Implicit Feedback Sequence Recommendation System Based on Interactive Interest Modeling

Traditional recommendation systems often rely on explicit feedback, such as user ratings and favorites. However, this approach suffers from data sparsity and inaccuracy issues. In contrast, implicit feedback data, such as user browsing history and click records, is much richer. Therefore, this paper uses implicit feedback data as the basis for recommendations, models user behavior to uncover their potential interests, and transforms them into vector representations.This paper also introduces interactive interest modeling. This approach models user interest representations based on implicit feedback user behavior sequences. For sequence recommendation problems, we use a Transformer model to extract deeper features from interactive interest representations and use an attention mechanism to capture users' real-time interests. Experimental results on Kaggle datasets demonstrate that this method outperforms traditional implicit feedback recommendation methods in terms of accuracy and personalization.