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information-retrieval icon information-retrieval

The goal of this programming assignment is to become familiar with Hadoop. We will do this in the context of information retrieval. Please monitor Canvas for example input and output, additional details, and links to resources. Information retrieval (IR) is concerned with finding material (e.g., documents) of an unstructured nature (usually text) in response to an information need (e.g., a query) from large collections. One approach to identify relevant documents is to compute scores based on the matches between terms in the query and terms in the documents. For example, a document with words such as ball, team, score, championship is likely to be about sports. It is helpful to define a weight for each term in a document that can be meaningful for computing such a score. We describe below popular information retrieval metrics such as term frequency, inverse document frequency, and their product, term frequency-inverse document frequency (TF-IDF), that are used to define weights for terms.

n-queen-problem icon n-queen-problem

The implementation of the N-Queen Problem using two approaches - Random Restart Hill Climbing and Min-Conflicts

pagerank icon pagerank

Computation of the PageRank for a predetermined set of webpages.

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