Informed recommender: basing recommendations on consumer product reviews

Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology

Exploratory factor analysis in validation studies : Uses and recommendations

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Recommendations for a mathematical curriculum to be used in conjunction with an oral deaf education program

Students who are deaf or hard of hearing have typically had difficulty in mathematics; however, this problem often is overlooked because of difficulties in language and reading abilities. This study aims to identify the most appropriate mathematics curriculum for deaf or hard of hearing students in an oral deaf education program.