2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2018)
Aug. 28, 2018 to Aug. 31, 2018
Panote Siriaraya , Kyoto Sangyo University, 6180, Japan
Yusuke Nakaoka , Kyoto Sangyo University, 6180, Japan
Yuanyuan Wang , Yamaguchi University, Japan
Yukiko Kawai , Kyoto Sangyo Univ. and Osaka Univ., Japan
This paper proposes a novel system which utilizes information from a social network services to suggest food venues to users based on crowd preferences. To recommend an appropriate food venue for each crowd preference, the system ranks food venues in each region by using an improved collaborative filtering method based on the differences between locations and languages in geo-tagged tweets. A key feature of the proposed system is the ability to suggest food venues in regions where very few geo-tagged tweets are available in a specific language by using the weighted similarity by others' preferences. To implement the system, more than 26 million tweets from European countries were collected and analyzed based on 6 languages and 7 regions. Afterwards, we provide an evaluation of the ranked venues proposed by the system based on 89 French speakers in 7 European countries.
Geo-tagged tweets, Multilingual analysis, Venue recommendation
P. Siriaraya, Y. Nakaoka, Y. Wang and Y. Kawai, "A Food Venue Recommender System Based on Multilingual Geo-Tagged Tweet Analysis," 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain, 2018, pp. 686-689.