Probabilistic Latent Semantic Indexing (PLSI) is a statistical approach to document indexing based on a latent class model for factor analysis of count data. Unlike standard Latent Semantic Indexing (LSI), which uses Singular Value Decomposition (SVD), PLSI has a solid statistical foundation and defines a proper generative data model. It can handle domain-specific synonymy and polysemous words, and has shown substantial performance gains over direct term matching and LSI in retrieval experiments. PLSI uses a probabilistic model with latent variables to represent documents and terms in a latent semantic space, allowing for more accurate representation of word usage and context. The model is fitted using a generalized Expectation Maximization (EM) algorithm, and can be combined with other models for improved performance. PLSI also allows for the use of term weighting schemes and has been shown to be more robust to overfitting than LSI. Experimental results demonstrate that PLSI outperforms LSI and standard term matching methods in terms of precision and recall. PLSI has also been applied to other areas such as language modeling and collaborative filtering. The method is based on the likelihood principle and defines a generative data model, allowing for the use of statistical methods for model fitting, overfitting control, and model combination. PLSI has been shown to be effective in a variety of applications, including document indexing, and has the potential to be used in other areas of machine learning and information retrieval.Probabilistic Latent Semantic Indexing (PLSI) is a statistical approach to document indexing based on a latent class model for factor analysis of count data. Unlike standard Latent Semantic Indexing (LSI), which uses Singular Value Decomposition (SVD), PLSI has a solid statistical foundation and defines a proper generative data model. It can handle domain-specific synonymy and polysemous words, and has shown substantial performance gains over direct term matching and LSI in retrieval experiments. PLSI uses a probabilistic model with latent variables to represent documents and terms in a latent semantic space, allowing for more accurate representation of word usage and context. The model is fitted using a generalized Expectation Maximization (EM) algorithm, and can be combined with other models for improved performance. PLSI also allows for the use of term weighting schemes and has been shown to be more robust to overfitting than LSI. Experimental results demonstrate that PLSI outperforms LSI and standard term matching methods in terms of precision and recall. PLSI has also been applied to other areas such as language modeling and collaborative filtering. The method is based on the likelihood principle and defines a generative data model, allowing for the use of statistical methods for model fitting, overfitting control, and model combination. PLSI has been shown to be effective in a variety of applications, including document indexing, and has the potential to be used in other areas of machine learning and information retrieval.