When: July 17, 2017 – 1:00pm – 3:00pm
Ms. Mahsa Daneshmandmehrabani will defend her M.Sc. on Monday July 17, 2017 at 1:00 PM in Mackenzie Chown C206.
Abstract: We develop a recommendation algorithm for a local entertainment and ticket pro-vider 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 differ-ent 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. Moreover, we provide a hybrid algorithm that utilizes content-based and col-laborative filtering recommendations. We also present an axiomatic analysis of our model. These axioms are mostly derived from social choice theory.
Location of your event: MCC206