The project builds a large-scale multimodal recommendation system that analyzes heterogeneous user interaction data and product content to deliver highly personalized recommendations across e-commerce and digital platforms. It integrates text, image, video, and audio modalities using multimodal LLM architectures, cross-attention fusion, and supervised ranking models.
Data Processing Layer
Ingest multimodal data, extract features per modality, normalize and align embedding.
Data: user purchase history, membership and behavioral logs, product text, image and video attributes, voice and audio queries, after-service feedback data
Representation Learning Layer
Unify multimodal embedding, use cross-attension fusion, encode user-product interaction.
(1) 30M+ text data embedding: use GPT to understand user intent from text data like queries and reviews, use semantic embedding to process product descriptions, generate context-aware personalization signals
(2) 1M+ speech data embedding: use Whisper to convert speech to text for voice queries, extract user intent from customer interactions transcription
(3) 10M+ vision data embedding: use CLIP for joint image-text embedding, extract product image feature, match visual similarity for recommendation ranking
(4) 1M+ video data embedding: extract frame-level features, use temporal representation of product videos, use temporal encoders for marketing content embedding for engagement prediction
(5) multimodal fusion: use cross-attention transformer layer to fuse text, speech, vision and video embeddings
Recommendation System
Use supervised ranking model (CTR prediction + preference scoring)to build hybrid retrieval and ranking system, generate personalized recommendation.