Improving Search Quality with Multimodal Embeddings and Federated Learning

The article discusses a novel approach to improve image search by leveraging multimodal embeddings and click-based metrics. The proposed solution combines two existing techniques, Multi-modal Item Embedding (MIEM) and Image-to-Text (I2T), to enhance the retrieval accuracy of images. MIEM creates vector representations for items based on their attributes, while I2T performs text-to-image retrieval using a pre-trained CLIP model. By combining these two techniques, the proposed solution can better capture the relationships between images and texts, leading to improved search accuracy.
The authors evaluate the performance of their proposed approach using a Shopee product test set with 3 million items. The results show that the combined MIEM + I2T model achieves the best performance in terms of recall at various rank positions. Moreover, the article analyzes the impact of different hyperparameters on the search accuracy and provides insights into the effectiveness of various click-based metrics.
In simple terms, the article presents a novel method to improve image search by combining two existing techniques: MIEM (which creates vector representations for items based on their attributes) and I2T (which performs text-to-image retrieval using a pre-trained CLIP model). By combining these two techniques, the proposed solution can better capture the relationships between images and texts, leading to improved search accuracy. The article provides detailed results from an evaluation using a Shopee product test set with 3 million items, demonstrating the effectiveness of the proposed approach.