Causal Feature-Enhanced Collaborative Filtering Algorithm

Recommendation systems are the primary means of information filtering in the current information age. They utilize user historical behaviors, personal preferences, and item attributes to predict user interests and deliver personalized recommendations. Existing research has predominantly focused on improving the accuracy of recommendation systems. However, false correlation and insufficient personalization are issues that have received limited attention. This paper introduces the Causal Feature-Enhanced Collaborative Filtering recommendation algorithm (CFECF) which addresses concerns that significantly impact the generalization capability of recommendation algorithms and user satisfaction. CFECF adopts a causal relationship perspective and introduces a latent outcome model framework to assess the true causal relationships between user and item features. This approach addresses the problem of false correlation in recommendation systems and enhances the model’s generalization capability. To improve personalization, user interaction histories are analyzed to derive a prior indicator based on statistical distribution, indicating user interest directions. The model’s training incorporates this indicator to enhance user satisfaction. Comprehensive experiments conducted on four real-world datasets affirm that the proposed CFECF markedly enhances recommendation performance when compared to state-of-the-art collaborative filtering methods, effectively addresses false correlation and insufficient personalization issues in recommendation systems.