On linear mixture of expert approaches to information retrieval

Knowledge intensive organizations have vast array of information contained in large document repositories. With the advent of E-commerce and corporate intranets/extranets, these repositories are expected to grow at a fast pace. This explosive growth has led to huge, fragmented, and unstructured document collections. Although it has become easier to collect and store information in document collections, it has become increasingly difficult to retrieve relevant information from these large document collections. Information Retrieval systems help users identify relevant documents for their information needs. Matching functions match the information in documents with that required by users in terms of queries to produce a set of documents to be presented to the users. It is well known that a single matching function does not produce the best retrieval results for all contexts (documents and queries). In this paper we combine the results obtained from well known matching functions in the literature. We employ Genetic Algorithms to do such combinations and test our method using a large well known document dataset. It is observed that our method produces better retrieval results for both the consensus search and the routing tasks in information retrieval.