COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual Recommendation

Understand

With the advancement of multimedia internet, the impact of visual characteristics on the decision of users to click or not within the online retail industry is increasingly significant.

• Thus, incorporating visual features is a promising direction for further performance improvements in click-through rate (CTR).
• However, experiments on our production system revealed that simply injecting the image embeddings trained with established pre-training methods only has marginal improvements.
• We believe that the main advantage of existing image feature pre-training methods lies in their effectiveness for cross-modal predictions.