Show simple item record

dc.contributor.authorDaneshmandmehrabani, Mahsa
dc.date.accessioned2017-08-21T13:53:10Z
dc.date.available2017-08-21T13:53:10Z
dc.identifier.urihttp://hdl.handle.net/10464/12897
dc.description.abstractWe develop a recommendation algorithm for a local entertainment and ticket provider company. The recommender system predicts the score of items, i.e. event, for each user. The special feature of these events, which makes them very different from similar settings, is that they are perishable: each event has a relatively short and specific lifespan. Therefore there is no explicit feedback available for a future event. Moreover, there is a very short description provided for each event and thus the keywords play a more than usual important role in categorizing each event. We provide a hybrid algorithm that utilizes content-based and collaborative filtering recommendations. We also present an axiomatic analysis of our model. These axioms are mostly derived from social choice theory.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectRecommendation Systemsen_US
dc.titleTowards a New Algorithm for Event Recommendation Systemen_US
dc.typeElectronic Thesis or Dissertationen
dc.degree.nameM.Sc. Mathematics and Statisticsen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Mathematicsen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US
refterms.dateFOA2021-08-01T01:40:09Z


Files in this item

Thumbnail
Name:
Brock_Daneshmandmehrabani_Mahs ...
Size:
2.377Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record