From reading minds to books: A dual-sided multimodal knowledge-aware recommender system based on collaborative contrastive learning

Key Points

• The recommender system improves accuracy through collaborative contrastive learning, with significant effects noted.
• Metrics indicate an increase in user satisfaction as the multimodal approach balances diverse inputs for recommendations.
• This analysis employs collaborative learning techniques to assess how integrated knowledge affects user engagement and recommendations.
• The findings support the potential of using multimodal algorithms in enhancing recommendation systems, calling for further exploration.