12,558 research outputs found
Location-aware online learning for top-k recommendation
We address the problem of recommending highly volatile items for users, both with potentially ambiguous location that may change in time. The three main ingredients of our method include (1) using online machine learning for the highly volatile items; (2) learning the personalized importance of hierarchical geolocation (for example, town, region, country, continent); finally (3) modeling temporal relevance by counting recent items with an exponential decay in recency.For (1), we consider a time-aware setting, where evaluation is cumbersome by traditional measures since we have different top recommendations at different times. We describe a time-aware framework based on individual item discounted gain. For (2), we observe that trends and geolocation turns out to be more important than personalized user preferences: user-item and content-item matrix factorization improves in combination with our geo-trend learning methods, but in itself, they are greatly inferior to our location based models. In fact, since our best performing methods are based on spatiotemporal data, they are applicable in the user cold start setting as well and perform even better than content based cold start methods. Finally for (3), we estimate the probability that the item will be viewed by its previous views to obtain a powerful model that combines item popularity and recency.To generate realistic data for measuring our new methods, we rely on Twitter messages with known GPS location and consider hashtags as items that we recommend the users to be included in their next message. © 2016 Elsevier B.V
Traveling Trends: Social Butterflies or Frequent Fliers?
Trending topics are the online conversations that grab collective attention
on social media. They are continually changing and often reflect exogenous
events that happen in the real world. Trends are localized in space and time as
they are driven by activity in specific geographic areas that act as sources of
traffic and information flow. Taken independently, trends and geography have
been discussed in recent literature on online social media; although, so far,
little has been done to characterize the relation between trends and geography.
Here we investigate more than eleven thousand topics that trended on Twitter in
63 main US locations during a period of 50 days in 2013. This data allows us to
study the origins and pathways of trends, how they compete for popularity at
the local level to emerge as winners at the country level, and what dynamics
underlie their production and consumption in different geographic areas. We
identify two main classes of trending topics: those that surface locally,
coinciding with three different geographic clusters (East coast, Midwest and
Southwest); and those that emerge globally from several metropolitan areas,
coinciding with the major air traffic hubs of the country. These hubs act as
trendsetters, generating topics that eventually trend at the country level, and
driving the conversation across the country. This poses an intriguing
conjecture, drawing a parallel between the spread of information and diseases:
Do trends travel faster by airplane than over the Internet?Comment: Proceedings of the first ACM conference on Online social networks,
pp. 213-222, 201
CommuniSense: Crowdsourcing Road Hazards in Nairobi
Nairobi is one of the fastest growing metropolitan cities and a major
business and technology powerhouse in Africa. However, Nairobi currently lacks
monitoring technologies to obtain reliable data on traffic and road
infrastructure conditions. In this paper, we investigate the use of mobile
crowdsourcing as means to gather and document Nairobi's road quality
information. We first present the key findings of a city-wide road quality
survey about the perception of existing road quality conditions in Nairobi.
Based on the survey's findings, we then developed a mobile crowdsourcing
application, called CommuniSense, to collect road quality data. The application
serves as a tool for users to locate, describe, and photograph road hazards. We
tested our application through a two-week field study amongst 30 participants
to document various forms of road hazards from different areas in Nairobi. To
verify the authenticity of user-contributed reports from our field study, we
proposed to use online crowdsourcing using Amazon's Mechanical Turk (MTurk) to
verify whether submitted reports indeed depict road hazards. We found 92% of
user-submitted reports to match the MTurkers judgements. While our prototype
was designed and tested on a specific city, our methodology is applicable to
other developing cities.Comment: In Proceedings of 17th International Conference on Human-Computer
Interaction with Mobile Devices and Services (MobileHCI 2015
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