Tackling the problems of diversity in recommender systems

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dc.contributor.author Karanam, Manikanta Babu
dc.date.accessioned 2010-12-15T15:11:51Z
dc.date.available 2010-12-15T15:11:51Z
dc.date.issued 2010-12-15
dc.identifier.uri http://hdl.handle.net/2097/6981
dc.description.abstract A recommender system is a computational mechanism for information filtering, where users provide recommendations (in the form of ratings or selecting items) as inputs, which the system then aggregates and directs to appropriate recipients. With the advent of web based media and publicity methods, the age where standardized methods of publicity, sales, production and marketing strategies do not. As such, in many markets the users are given a wide range of products and information to choose which product they like, to find a way out of this recommender systems are used in a way similar to the live social scenario, that is a user tries to get reviews from friends before opting for a product in a similar way recommender system tries to be a friend who recommends the options. Most of the recommender systems currently developed solely accuracy driven, i.e., reducing the Mean Absolute Error (MAE) between the predictions of the recommender system and actual ratings of the user. This leads to various problems for recommender systems such as lack of diversity and freshness. Lack of diversity arises when the recommender system is overly focused on accuracy by recommending a set of items, in which all of the items are too similar to each other, because they are predicted to be liked by the user. Lack of freshness also arises with overly focusing on accuracy but as a limitation on the set of items recommended making it overly predictable. This thesis work is directed at addressing the issues of diversity, by developing an approach, where a threshold of accuracy (in terms of Mean Absolute Error in prediction) is maintained while trying to diversify the set of item recommendations. Here for the problem of diversity a combination of Attribute-based diversification and user preference based diversification is done. This approach is then evaluated using non-classical methods along with evaluating the base recommender algorithm to prove that diversification is indeed is possible with a mixture of collaborative and content based approach. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Recommender Systems en_US
dc.subject Diversity en_US
dc.subject Collaborative Filtering en_US
dc.subject Content Based Filtering en_US
dc.subject Hybrid Systems en_US
dc.title Tackling the problems of diversity in recommender systems en_US
dc.type Thesis en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Computing and Information Sciences en_US
dc.description.advisor William H. Hsu en_US
dc.subject.umi Computer Science (0984) en_US
dc.date.published 2010 en_US
dc.date.graduationmonth December en_US

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