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Question 5 12 points Save Answer Part A -10 pts Consider an IR system that uses the Rocchio algorithm for gathering and using relevance feedback from uses. The initial query of the user is "cheap CDs extremely cheap DVDs". Let the collection contain just three documents d1, d2 and d3. Among the retrieved results, the user provides positive feedback on document d1 (i.e. considers it to be relevant) and negative feedback on document d2 and d3 (i.e. considers them to be relevant). The content of the documents are: d1: CDs cheap software cheap CDs d2: cheap thrills DVDs d3: CDs DVDs Construct the document vector by using the raw term frequency as the weights of the terms when generating the vectors. No need to calculate the idf of the terms. There is no need to length-normalize vectors. Using Rocchio relevance feedback, what would the revised query vector be after relevance feedback? Assume alpha- bta 0.75, gamma- 0.25. The weights of the following terms in the new query are: CDs: Cheap: Software: DVDs: Question 5 12 points Save Answer Part A -10 pts Consider an IR system that uses the Rocchio algorithm for gathering and using relevance feedback from uses. The initial query of the user is "cheap CDs extremely cheap DVDs". Let the collection contain just three documents d1, d2 and d3. Among the retrieved results, the user provides positive feedback on document d1 (i.e. considers it to be relevant) and negative feedback on document d2 and d3 (i.e. considers them to be relevant). The content of the documents are: d1: CDs cheap software cheap CDs d2: cheap thrills DVDs d3: CDs DVDs Construct the document vector by using the raw term frequency as the weights of the terms when generating the vectors. No need to calculate the idf of the terms. There is no need to length-normalize vectors. Using Rocchio relevance feedback, what would the revised query vector be after relevance feedback? Assume alpha- bta 0.75, gamma- 0.25. The weights of the following terms in the new query are: CDs: Cheap: Software: DVDs