Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Latent Semantic Indexing (LSI) has been widely used in information retrieval due to its efficiency in solving the problems of polysemy and synonymy. Hover, LSI is notably a computationally intensive process because of the computing complexities of singular value decomposition and filtering operations involved in the process.  presents MR-LSI, a MapReduce based distributed LSI algorithm for scalable information retrieval. The performance of MR-LSI is first evaluated in a small scale experimental cluster environment, and subsequently evaluated in large scale simulation environments. By partitioning the dataset into smaller subsets and optimizing the partitioned subsets across a cluster of computing nodes, the overhead of the MR-LSI algorithm is reduced significantly while maintaining a high level of accuracy in retrieving documents of user interest. A genetic algorithm based load balancing scheme is designed to optimize the performance of MR-LSI in heterogeneous computing environments in which the computing nodes have varied resources.
['Yang Liu', 'Maozhen Li', 'Mukhtaj Khan', 'Man Qi']
I am writing a research paper on a idea A MAPREDUCE BASED DISTRIBUTED LSI FOR SCALABLE INFORMATION RETRIEVAL
[{"from": "human", "value": "I am writing a research paper on a idea A MAPREDUCE BASED DISTRIBUTED LSI FOR SCALABLE INFORMATION RETRIEVAL"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Latent Semantic Indexing (LSI) has been widely used in information retrieval due to its efficiency in solving the problems of polysemy and synonymy. Hover, LSI is notably a computationally intensive process because of the computing complexities of singular value decomposition and filtering operations involved in the process.  presents MR-LSI, a MapReduce based distributed LSI algorithm for scalable information retrieval. The performance of MR-LSI is first evaluated in a small scale experimental cluster environment, and subsequently evaluated in large scale simulation environments. By partitioning the dataset into smaller subsets and optimizing the partitioned subsets across a cluster of computing nodes, the overhead of the MR-LSI algorithm is reduced significantly while maintaining a high level of accuracy in retrieving documents of user interest. A genetic algorithm based load balancing scheme is designed to optimize the performance of MR-LSI in heterogeneous computing environments in which the computing nodes have varied resources."}]