Understanding Multilingual Correlation of Geo-Tagged Tweets for POI Recommendation
Understanding Multilingual Correlation of Geo-Tagged Tweets for POI Recommendation
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DOI：
10.1007/978-3-030-60952-8_14
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Yuanyuan Wang;Panote Siriaraya;Mohit Mittal;Huaze Xie;Yukiko Kawai
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文献类型：
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作者：
Yuanyuan Wang;Panote Siriaraya;Mohit Mittal;Huaze Xie;Yukiko Kawai
本文提出了一种基于心理偏好推荐POI的推特多语言分析方法。属于不同国家的人有不同的行为活动，说不同的语言。例如，根据心理分析，访问其他国家的人对吃本国的食物感兴趣。为此，我们的目标是通过分析基于时间、地点和语言的地理标记推文来澄清用户行为的心理偏好。在这项工作中，我们关注了来自欧洲国家的地理标记推文中的地点和语言之间的差异。拟议系统的一个关键特征是，通过使用按其他人的偏好加权的相似性，能够在很少有特定语言的地理标记推文的地区建议(针对游客的)POI。具体地说，我们首先提取tweet的语言，然后根据tweet位置的纬度和经度识别tweet所在的国家。然后，我们使用基于ATF-idf的方法从特定区域的特定语言的推文中提取特征词。本文基于区域内推文特征词之间的语言相关性，讨论了不同语言用户的POI偏好。
This paper presents a multilingual analysis of Twitter for recommending POIs based on psychographic preferences. People who belong to different countries have different behavioral activities and speak different languages. According to psychographic analysis, for example, people who visit other countries are interested in eating the food of their home country. For this, we aim to clarify psychographic preferences for user behaviors by analyzing geo-tagged tweets based on times, locations, and languages. In this work, we focused on the differences between locations and languages in geo-tagged tweets from European countries. A key feature of the proposed system is the ability to suggest POIs (for tourists) in regions where very few geo-tagged tweets are available in a specific language by using the weighted similarity by others’ preferences. Specifically, we first extract languages of tweets, and we identify tweeting countries based on the latitude and longitude of tweet locations. Then, we extract feature words from tweets of a specific language in a specific region by using atf-idfbased approach. In this paper, we discuss the POI preferences of different language users based on the linguistic correlation between feature words of tweets in the region.