Matrix Factorization and Latent Semantic Indexing Vector Space model: Pros Automatic selection of index terms Partial matching of queries and documents(dealing with the case where no document contains all search terms Ranking according to similarity score (dealing with large result sets) Term weighting schemes (improves retrieval performance) Various extensions Document clustering Relevance feedback ( modifying query vector) Geometric foundatⅰon 26Matrix Factorization and Latent Semantic Indexing 26 Vector Space Model: Pros ▪ Automatic selection of index terms ▪ Partial matching of queries and documents (dealing with the case where no document contains all search terms) ▪ Ranking according to similarity score (dealing with large result sets) ▪ Term weighting schemes (improves retrieval performance) ▪ Various extensions ▪ Document clustering ▪ Relevance feedback (modifying query vector) ▪ Geometric foundation LSI