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An Evaluation Of The Use Of Diversity To Improve The Accuracy Of Predicted Ratings In Recommender Systems, Gillian Browne
An Evaluation Of The Use Of Diversity To Improve The Accuracy Of Predicted Ratings In Recommender Systems, Gillian Browne
Dissertations
The diversity; versus accuracy trade off, has become an important area of research within recommender systems as online retailers attempt to better serve their customers and gain a competitive advantage through an improved customer experience. This dissertation attempted to evaluate the use of diversity measures in predictive models as a means of improving predicted ratings. Research literature outlines a number of influencing factors such as personality, taste, mood and social networks in addition to approaches to the diversity challenge post recommendation. A number of models were applied included DecisionStump, Linear Regression, J48 Decision Tree and Naive Bayes. Various evaluation metrics …