Item2Vec (Sequential)

Description

The Item2Vec policy adapts the Word2Vec algorithm (CBOW or Skip-gram) to learn item embeddings from user interaction sequences. It captures item co-occurrence patterns within a defined context window, placing items frequently interacted with together closer in the embedding space. Primarily models similarity based on co-occurrence rather than strict sequential order.

Policy Type: item2vec Supports: embedding_policy, scoring_policy

Configuration Example

scoring_policy_item2vec.yaml

policy_configs:
  scoring_policy: 
    policy_type: item2vec
    embedding_size: 512  # Dimensionality of the learned item embeddings
    window_size: 20      # Context window size (items before/after)
    min_count: 1         # Minimum frequency for an item to be included
    algorithm: "cbow"    # Training algorithm ('cbow' or 'skipgram')

References

• Mikolov, T., et al. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv. (Foundation Word2Vec).
• Barkan, O., & Koenigstein, N. (2016). Item2vec: neural item embedding for collaborative filtering. arXiv.