Landscape recommendation system using public preference mining and social influence analysis

Wen Hao Tsai, Yan Ting Lin, Kuan Rung Lee, Yau Hwang Kuo, Bing Huei Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


A novel landscape recommendation system which employs public preference and social influence to classify user preference orientation is proposed in this paper. Unlike traditional content-based or collaborative filtering recommendation approaches, we collected large scale information from heterogeneous data sources to construct the public preference model for user's feature-based preference orientation classification. Moreover, the social relation graph of target user is constructed to analyze social influence of preference between users in it. Then, the social influence of preference is calculated by social influence and interest similarity between users. The purpose of this paper is that using public preference to infer user preference and further adjusting user preference through social influence of preference from neighbors. The proposed method deals with the cold-start issue in recommendation system. There two main advantages of the proposed method are social relationship can be easily obtained from online social network and any type of recommendation system can be applied in the proposed method. In our experiment, Facebook, the most famous social media, is the platform selected for social relationship analysis. The experimental result shows our approach not only innovation but also practicable.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014
EditorsWilliam Cheng-Chung Chu, Han-Chieh Chao, Stephen Jenn-Hwa Yang
PublisherIOS Press BV
Number of pages10
ISBN (Electronic)9781614994831
Publication statusPublished - 2015
EventInternational Computer Symposium, ICS 2014 - Taichung, Taiwan
Duration: 2014 Dec 122014 Dec 14

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314


OtherInternational Computer Symposium, ICS 2014

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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