A New Recommendation Framework for Accommodations Sharing Based on User Preference

  • Qiuyan ZhongEmail author
  • Yangguang Wang
  • Yueyang Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 660)


The population of accommodations sharing makes it necessary to make personalized recommendations for users. But general methods perform poorly when applied to accommodations sharing due to severer sparse data and user stickiness. In order to conquer these gaps, this study proposes a specific framework with considering the information of user preference through LDA and Naive Bayes Method. These two methods enable to transfer the user preference into quantitative analysis. Furthermore, LFM possesses the function that can forecast the missing parts through learning user-item rating matrices. It generates the first recommendation list based on the quantized characterization of user preference. The second recommendation list can produce by the Naive Bayes Method. Consequently, the final optimal recommendation result of accommodation sharing is gathered from combining two recommendation lists. Finally, experiments based on the Airbnb real dataset demonstrate the promising potential of this study.


Accommodations sharing Recommendation system Latent Factor Model (LFM) Latent Dirichlet Allocation (LDA) User preference analysis 



This research is financially supported by NSFC with its projects (71533001).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  1. 1.Faculty of Management and EconomicsDalian University of TechnologyDalianPeople’s Republic of China

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