Document Type

Article

Language

eng

Publication Date

9-10-2018

Publisher

Institute of Electrical and Electronic Engineers (IEEE)

Source Publication

2018 IEEE International Congress on Big Data (BigData Congress)

Source ISSN

9781538672327

Abstract

Personalization of recommender systems enables customized services to users. Social media is one resource that aids personalization. This study explores the use of twitter data to personalize travel recommendations. A machine learning classification model is used to identify travel related tweets. The travel tweets are then used to personalize recommendations regarding places of interest for the user. Places of interest are categorized as: historical buildings, museums, parks, and restaurants. To better personalize the model, travel tweets of the user's friends and followers are also mined. Volunteer twitter users were asked to provide their twitter handle as well as rank their travel category preferences in a survey. We evaluated our model by comparing the predictions made by our model with the users choices in the survey. The evaluations show 68% prediction accuracy. The accuracy can be improved with a better travel-tweet training dataset as well as a better travel category identification technique using machine learning. The travel categories can be increased to include items like sports venues, musical events, entertainment, etc. and thereby fine-tune the recommendations. The proposed model lists 'n' places of interest from each category in proportion to the travel category score generated by the model.

Comments

Accepted version. 2018 IEEE International Congress on Big Data (BigData Congress), (September 10, 2018). DOI. © 2018 Institute of Electrical and Electronic Engineers (IEEE). Used with permission.

Download
Madiraju_13325acc.docx (109 kB)
ADA Accessible Version

Share

COinS