Optimizing Content Recommendations Using a Hybrid Filtering Algorithm to Enhance User Relevance and Engagement

Abstract

Recommender systems play an important role in helping users discover relevant content in environments characterized by information overload. However, existing approaches often struggle to balance recommendation relevance and user engagement. Collaborative filtering is constrained by data sparsity and the cold-start problem, whereas content-based methods that rely on textual features may not fully capture dynamic user preferences. This study aims to develop a hybrid deep learning-based recommendation model that improves both recommendation relevance and user engagement. The proposed method integrates collaborative filtering via Neural Matrix Factorization (NeuMF) with content-based filtering via a Long Short-Term Memory (LSTM) text encoder, employing an early-fusion strategy. An experimental research method was applied using synthetic user–item interaction data. Model performance was evaluated using ranking metrics (Precision@10, Recall@10, and NDCG@10) and engagement metrics (Click-Through Rate and Average Completion Ratio). The results show that the hybrid model outperforms the baseline models. It achieves Precision@10 of 0.143, Recall@10 of 0.112, and NDCG@10 of 0.139, which exceed those of both the NeuMF-only and LSTM-only models. In terms of engagement, the hybrid model also records the best performance with a CTR of 0.0017 and an ACR of 0.0090. These findings indicate that integrating user–item interaction patterns with semantic content representations can significantly enhance recommendation quality and user engagement, providing a more effective solution for content-rich digital platforms.