Sparse-Matrix Factorization-based Method: Efficient Computation of Latent Query and Item Representations to Approximate CE Scores

Cross-Encoder Models for Efficient Query-Item Similarity Evaluation Cross-encoder (CE) models are a powerful tool for evaluating how similar a query is to an item. They outperform traditional methods and provide better estimates of query-item relevance. Practical Solutions and Value Our sparse-matrix factorization-based method efficiently computes latent query and item representations to approximate CE scores. This allows for optimal k-NN search using the approximate CE similarity, resulting in high-quality approximations with fewer CE similarity calls. By aligning item embeddings with the cross-encoder, significant improvements in k-NN recall and speedup over baseline methods are achieved. Matrix Factorization for Sparse Matrices Matrix factorization is a widely used technique for evaluating low-rank approximation of matrices and recovering missing entries. Our method optimally computes latent query and item representations, efficiently approximates CE scores, and enhances k-NN search performance. Practical Solutions and Value The method introduced by the University of Massachusetts Amherst and Google DeepMind has been shown to be effective in tasks like zero-shot entity linking and information retrieval. It optimizes the recovery of missing entries when features describing the matrix’s rows and columns are available. AI Implementation Recommendations Discover how AI can transform your work processes. Identify Automation Opportunities, Define KPIs, Select an AI Solution, and Implement Gradually. For AI KPI management advice, connect with us at hello@itinai.com. Spotlight on a Practical AI Solution Consider the AI Sales Bot from itinai.com/aisalesbot designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. List of Useful Links: AI Lab in Telegram @itinai – free consultation Twitter – @itinaicom