DSpace Repository

Ensemble techniques for predicting the consumer rating of movies

Show simple item record

dc.contributor.advisor Guha, Sumanta (Chairperson) en_US
dc.contributor.author Bhattarai, Suresh Raj en_US
dc.contributor.other Phan Minh Dung (Member) en_US
dc.contributor.other Janecek, Paul (Member) en_US
dc.date.accessioned 2015-01-12T10:43:52Z
dc.date.available 2015-01-12T10:43:52Z
dc.date.issued 2010-05 en_US
dc.identifier.other AIT RSPR no.IM-10-03 en_US
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/479
dc.description Submitted in partial fulfillment of the requirements for the degree of Masters of Science in Information Management. en_US
dc.description.abstract Making a match of appropriate products to appropriate consumers has become a key to success in business since it helps in providing higher consumer satisfaction. That’s why, personalized services with the best recommendation system has been in high demand. One of the key factors for the success of best recommendation lies in the accuracy of the system. The more the system is accurate, the more likely will be the system’s ability to recommend with the most appropriate product. This research is based on AusDM 2009 Analytic Challenge where the study and analysis was made on the data provided by Netflix Inc. so as to make an innovative effort of improving the accuracy of existing online movie recommendation system. This study is more focused on using ensemble techniques for increasing the performance of the system in order to predict the consumer movie ratings more accurately. The main objective of this research study and also of AusDM challenge is to use the data provided by Netflix Inc. in order to combine the individual model predictors to increase the overall performance of the recommendation system. This study presents ensemble approaches like Bagging, Hill Climbing and Adaboost and makes the comparative analysis of those methodologies. en_US
dc.description.sponsorship ADB - Japan Scholarship Program (ADB-JSP) en_US
dc.language.iso eng en_US
dc.publisher Asian Institute of Technology en_US
dc.subject Ratings en_US
dc.subject Adaboost en_US
dc.subject Recommendation system en_US
dc.subject.lcsh Others en_US
dc.title Ensemble techniques for predicting the consumer rating of movies en_US
dc.type Research Report en_US
dc.rights.holder Copyright (C) 2010 by Asian Institute of Technology. en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account