optimization Vector Product Vector Vector

GitHub Repo https://github.com/MurtazaAbidi/Vector-Space-Model

MurtazaAbidi/Vector-Space-Model

The query processing of VSM is quite tricky, you need of optimize every aspect of computation. The high-dimensional vector product and similarity values of query (q) and documents (d) need to optimized.

GitHub Repo https://github.com/Thaneshwar-sahu/Price-Feature_Vector_Analysis_for_Beverage_Mug_Product_Optimization

Thaneshwar-sahu/Price-Feature_Vector_Analysis_for_Beverage_Mug_Product_Optimization

This project aims to optimize the price-feature vector for a beverage mug line, identifying the best combination of price and features to enhance market competitiveness. The goal is to determine which features and price points attract consumers, helping to create a product that balances consumer preferences with market trends.

GitHub Repo https://github.com/Paniz-Peiravani/Optimization-of-Dot-Product

Paniz-Peiravani/Optimization-of-Dot-Product

Optimization of dot product computation of two vectors using vector instructions.

GitHub Repo https://github.com/unais5/Vector-Space-Model

unais5/Vector-Space-Model

The query processing of VSM is quite tricky, you need of optimize every aspect of computation. The high-dimensional vector product and similarity values of query (q) and documents (d) need to optimized. Basic Assumption for Vector Space Model (VSM) Retrieval Model 1.Simple model based on linear algebra. Terms are considered as features using a weighting scheme. 2.Allows partial matching of documents with the queries. Hence, able to produce good institutive scoring. Continuous scoring between queries and documents. 3.Ranking of documents are possible using relevance score between document and query.

GitHub Repo https://github.com/berenger-eu/spc5

berenger-eu/spc5

Highly optimized sparse matrix vector product (SpMV) for AVX512.

GitHub Repo https://github.com/Ankurx7/NLP_basedSearchPlatform_v2

Ankurx7/NLP_basedSearchPlatform_v2

NLP-powered search platform leveraging WinkNLP, cosine similarity vectors, and intelligent semantic analysis. Features multi-modal search architecture with Elasticsearch and MongoDB integration, fuzzy matching with Fuse.js, and sophisticated re-ranking systems for context-aware product discovery and superior search relevance optimization.

GitHub Repo https://github.com/SENATOROVAI/singular-value-decomposition-svd-solver-course

SENATOROVAI/singular-value-decomposition-svd-solver-course

Singular Value Decomposition (SVD) is a fundamental linear algebra technique that factorizes any into the product of three matrices: are orthogonal matrices containing left and right singular vectors, while sigma is a diagonal matrix of non-negative singular values. It is essential for data reduction, noise removal, and matrix approximation.Solver

GitHub Repo https://github.com/carlosdoalgarve/DotProduct