Title page for etd-0621117-124624


[Back to Results | New Search]

URN etd-0621117-124624
Author Yao-qing Huang
Author's Email Address No Public.
Statistics This thesis had been viewed 5344 times. Download 63 times.
Department Information Management
Year 2017
Semester 1
Degree Master
Type of Document
Language English
Title LDA-Based Personalized recommendation for Airbnb
Date of Defense 2017-07-21
Page Count 49
Keyword
  • Airbnb
  • Recommender system
  • Text mining
  • LDA
  • Topic model
  • Abstract Airbnb is one of the most successful sharing economy platforms in the hospitality industry. Although the availability of large-scale reviews can be beneficial but it is more difficult in the decision-making process, because of the huge amount of reviews which make guests confused in selecting the best possible and suitable properties.
    In this thesis, we propose a personalized recommender system by applying LDA to extract latent topics of textual resource of each property and use the probability of topic distribution to represent the features of each property. Further, construct guest profile based on guest’s historical records in order to realize guest preference. Finally, for each candidate property, we consider the profiles of property and guest to estimate a sorted recommend list for the guest.
    For the evaluation, we adopt Recall to evaluate the recommendation performance. The experimental result shows that our LDA-based model performs better than the baseline. Afterwards, we compare the performance among different textual information which shows the review and rating score are appropriate resource for the property representation and guest preference on the LDA-based personalized recommender system.
    Advisory Committee
  • Chih-Hua Tai - chair
  • Cheng-Jung Yang - co-chair
  • Keng-Pei Lin - advisor
  • Files
  • etd-0621117-124624.pdf
  • Indicate in-campus at 1 year and off-campus access at 1 year.
    Date of Submission 2017-10-19

    [Back to Results | New Search]


    Browse | Search All Available ETDs

    If you have more questions or technical problems, please contact eThesys