Stages in Search & Recommendation Systems

Item Embedding Pretraining

  • Goal: - Learn dense item embeddings useful for retrieval, search, and recommendation. - Build item index for search and homepage recsys - Cold-start robustness by using rich content + weak interaction signals

  • Signals:

    • Item2Vec or CBOW-style pretraining using session data

    • Co-engagement graphs: Co-liked, co-favorited, co-contacted seller

    • Cross-platform metadata: seller network embeddings, profile stats

  • Modalities & Features:

    • Text (title, description)

    • Image/video (thumbnail, listing video)

    • Metadata (category, tags, location)

    • Author features (demographics, group activity, reputation)

  • Embedding Spaces:

    • Separate: one for retrieval, one for semantic search

    • Joint (with fine-tuning for task-specific objectives)

User Embedding for Retrieval

  • Goal: Personalize retrieval using long-term user behavior

  • Retrieval embeddings = similarity optimized

  • Challenges: Sparse on-platform interactions

  • Signals:

    • Watch/browse/contact history on other Meta surfaces for cross-platform activity mining: pages liked, groups joined

    • Facebook friends/groups/interests

    • Location, device, demographics

    • Seller interaction graphs (contacted, purchased from)

  • Models:

    • DSSM-style dual tower models (user tower with user profile + behavior history + item tower)

    • Pretrained item embeddings reused from Step 5

    • InfoNCE with in-batch negatives

Ranking

  • Goal: Rank retrieved items based on relevance, intent, quality, and engagement likelihood.

  • Models:

    • Wide & Deep, DeepFM (crossed features)

    • BST (Behavioral Sequence Transformer)

    • Multitask models (CTR, message likelihood, time spent)

  • Features:

    • User features: demographics, location, device, time, profile

    • User behavior: short-term session sequences (click/view/pause), long-term interest

    • Item features: embeddings, quality score, popularity, recency

    • Contextual signals: entry point, seasonality, intent cues

    • Seller: profile, reputation

  • Embedding Reuse:

    • Ranking embeddings = intent + context optimized

    • Reuse item embeddings from pretraining as input features

    • Reuse user embeddings from retrieval model

    • Fine-tune during ranking task training