Sharing accommodation has emerged recently as a new business model in the accommodation sector. Due to the potential gentrification Airbnb might bring to an area, it is critical to understand the spatial patterns of sharing economy and its possible determinants. The neighbourhood environment has proven to be an important factor in the traditional hotel business, and whether it is the same for sharing accommodation is worth investigating. In this study, location data of 29,780 houses/apartments on Airbnb.com in London was collected. Using Ordinal Least Square and Geography Weighed Regression analysis, the spatial distribution features of Airbnb and its relationship with neighbourhood environment in London were explored. The results show that sharing accommodation is mainly located in the city centre and around tourist attractions. Neighbourhood elements such as Water, Vegetation Coverage, Art & Human Landscape, Travel & Transport, University, Nightlife Spot emerged as important factors influencingAirbnb. In addition, the distribution of Airbnb in London is spatially nonstationary, in some areas high Airbnb is associated with higher transportation accessibility, in other areas, high Airbnb is associated with more attractions or nightlife spots, suggesting that the role of different factors varies in different regions, proving Tobler's first law of geography.
Social recommender systems aim to support user preferences and help users make better decisions in social media. The social network and the social context are two vital elements in social recommender systems. In this contribution, we propose a new framework for a social recommender system based on both network structure analysis and social context mining. Exponential random graph models (ERGMs) are able to capture and simulate the complex structure of a micro-blog network. We derive the prediction formula from ERGMs for recommending micro-blog users. Then, a primary recommendation list is created by analysing the micro-blog network structure. In the next step, we calculate the sentiment similarities of micro-blog users based on a sentiment feature set which is extracted from users’ tweets. Sentiment similarities are used to filter the primary recommendation list and find users who have similar attitudes on the same topic. The goal of those two steps is to make the social recommender system much more precise and to satisfy users’ psychological preferences. At the end, we use this new framework deal with big real-world data. The recommendation results of diabetes accounts of Weibo show that our method outperforms other social recommender systems.
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