30,761 research outputs found
You are What you Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food & Drink Habits in Foursquare
Food and drink are two of the most basic needs of human beings. However, as
society evolved, food and drink became also a strong cultural aspect, being
able to describe strong differences among people. Traditional methods used to
analyze cross-cultural differences are mainly based on surveys and, for this
reason, they are very difficult to represent a significant statistical sample
at a global scale. In this paper, we propose a new methodology to identify
cultural boundaries and similarities across populations at different scales
based on the analysis of Foursquare check-ins. This approach might be useful
not only for economic purposes, but also to support existing and novel
marketing and social applications. Our methodology consists of the following
steps. First, we map food and drink related check-ins extracted from Foursquare
into users' cultural preferences. Second, we identify particular individual
preferences, such as the taste for a certain type of food or drink, e.g., pizza
or sake, as well as temporal habits, such as the time and day of the week when
an individual goes to a restaurant or a bar. Third, we show how to analyze this
information to assess the cultural distance between two countries, cities or
even areas of a city. Fourth, we apply a simple clustering technique, using
this cultural distance measure, to draw cultural boundaries across countries,
cities and regions.Comment: 10 pages, 10 figures, 1 table. Proceedings of 8th AAAI Intl. Conf. on
Weblogs and Social Media (ICWSM 2014
Interacting Attention-gated Recurrent Networks for Recommendation
Capturing the temporal dynamics of user preferences over items is important
for recommendation. Existing methods mainly assume that all time steps in
user-item interaction history are equally relevant to recommendation, which
however does not apply in real-world scenarios where user-item interactions can
often happen accidentally. More importantly, they learn user and item dynamics
separately, thus failing to capture their joint effects on user-item
interactions. To better model user and item dynamics, we present the
Interacting Attention-gated Recurrent Network (IARN) which adopts the attention
model to measure the relevance of each time step. In particular, we propose a
novel attention scheme to learn the attention scores of user and item history
in an interacting way, thus to account for the dependencies between user and
item dynamics in shaping user-item interactions. By doing so, IARN can
selectively memorize different time steps of a user's history when predicting
her preferences over different items. Our model can therefore provide
meaningful interpretations for recommendation results, which could be further
enhanced by auxiliary features. Extensive validation on real-world datasets
shows that IARN consistently outperforms state-of-the-art methods.Comment: Accepted by ACM International Conference on Information and Knowledge
Management (CIKM), 201
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