Towards a New Algorithm for Event Recommendation System

We 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.

Name thesis:degree_name

M.Sc. Mathematics and Statistics

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Faculty of Mathematics and Science

Department dc:contributor.department

Department of Mathematics

Grantor

Brock University

Year dc:date.issued

2017

Author dc:creator

• Daneshmandmehrabani, Mahsa

• Recommendation Systems

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/10464/12897

OAI identifier oai:identifier

oai:brocku.scholaris.ca:10464/12897