69,871 research outputs found
Gender Matters! Analyzing Global Cultural Gender Preferences for Venues Using Social Sensing
Gender differences is a phenomenon around the world actively researched by
social scientists. Traditionally, the data used to support such studies is
manually obtained, often through surveys with volunteers. However, due to their
inherent high costs because of manual steps, such traditional methods do not
quickly scale to large-size studies. We here investigate a particular aspect of
gender differences: preferences for venues. To that end we explore the use of
check-in data collected from Foursquare to estimate cultural gender preferences
for venues in the physical world. For that, we first demonstrate that by
analyzing the check-in data in various regions of the world we can find
significant differences in preferences for specific venues between gender
groups. Some of these significant differences reflect well-known cultural
patterns. Moreover, we also gathered evidence that our methodology offers
useful information about gender preference for venues in a given region in the
real world. This suggests that gender and venue preferences observed may not be
independent. Our results suggests that our proposed methodology could be a
promising tool to support studies on gender preferences for venues at different
spatial granularities around the world, being faster and cheaper than
traditional methods, besides quickly capturing changes in the real world
Context Embedding Networks
Low dimensional embeddings that capture the main variations of interest in
collections of data are important for many applications. One way to construct
these embeddings is to acquire estimates of similarity from the crowd. However,
similarity is a multi-dimensional concept that varies from individual to
individual. Existing models for learning embeddings from the crowd typically
make simplifying assumptions such as all individuals estimate similarity using
the same criteria, the list of criteria is known in advance, or that the crowd
workers are not influenced by the data that they see. To overcome these
limitations we introduce Context Embedding Networks (CENs). In addition to
learning interpretable embeddings from images, CENs also model worker biases
for different attributes along with the visual context i.e. the visual
attributes highlighted by a set of images. Experiments on two noisy crowd
annotated datasets show that modeling both worker bias and visual context
results in more interpretable embeddings compared to existing approaches.Comment: CVPR 2018 spotligh
Spatial Concentration of Institutional Property Ownership: New Wave Atomistic or Traditional Urban Clustering
NCREIF investors acquire property in counties that meet socioeconomic filtering criteria. In contrast to atomistic predictions, these investors acquire their apartment buildings, offices, retail facilities, and warehouses in density clusters. These clusters follow a model of a negative exponential demand curve, a model that previously explained the technologically caused density gradient of urban areas. Institutional investors signal their belief that clustering of properties is a value dimension.
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making
In multi-objective decision planning and learning, much attention is paid to
producing optimal solution sets that contain an optimal policy for every
possible user preference profile. We argue that the step that follows, i.e,
determining which policy to execute by maximising the user's intrinsic utility
function over this (possibly infinite) set, is under-studied. This paper aims
to fill this gap. We build on previous work on Gaussian processes and pairwise
comparisons for preference modelling, extend it to the multi-objective decision
support scenario, and propose new ordered preference elicitation strategies
based on ranking and clustering. Our main contribution is an in-depth
evaluation of these strategies using computer and human-based experiments. We
show that our proposed elicitation strategies outperform the currently used
pairwise methods, and found that users prefer ranking most. Our experiments
further show that utilising monotonicity information in GPs by using a linear
prior mean at the start and virtual comparisons to the nadir and ideal points,
increases performance. We demonstrate our decision support framework in a
real-world study on traffic regulation, conducted with the city of Amsterdam.Comment: AAMAS 2018, Source code at
https://github.com/lmzintgraf/gp_pref_elici
A SEGMENTATION ANALYSIS OF U.S. GROCERY STORE SHOPPERS
Cluster analysis was used to conduct a segmentation analysis of U.S. supermarket shoppers. This study is based on the responses of a sample of 1,000 shoppers concerning the importance of 21 store characteristics in selecting their primary grocery store for the Food Marketing Institute's 2000 consumer trends survey. Stores must satisfy the attributes important to all consumers in order to be successful. In order of importance, the four top characteristics are a clean/neat store, high quality produce, high quality meats and courteous, friendly employees. The three key supermarket shopper segments identified are time-pressed convenience seekers, sophisticates, and middle Americans. In order to cater to a particular consumer niche, a store must better fulfill the store preferences of that segment. Time-pressed convenience seekers, 36.70 percent of the sample, put a premium on features such as childcare, gas pumps and online shopping. They are likely to be younger, urban with lower or moderate incomes and have the greatest number of children six years old or younger. Quality and services are important to the sophisticates, 28.40 percent of the sample. This group is middle-aged, better educated with higher incomes than average. Middle Americans, 34.90 percent, are attracted by pricing/value factors such as frequent shopper programs, sales and private label brands. They want stores that are active in the community. Demographically they are in the middle with the highest proportion of high school graduates.Consumer/Household Economics, Food Consumption/Nutrition/Food Safety, Marketing,
Caching Improvement Using Adaptive User Clustering
In this article we explore one of the most promising technologies for 5G
wireless networks using an underlay small cell network, namely proactive
caching. Using the increase in storage technologies and through studying the
users behavior, peak traffic can be reduced through proactive caching of the
content that is most probable to be requested. We propose a new method, in
which, instead of caching the most popular content, the users within the
network are clustered according to their content popularity and the caching is
done accordingly. We present also a method for estimating the number of
clusters within the network based on the Akaike information criterion. We
analytically derive a closed form expression of the hit probability and we
propose an optimization problem in which the small base stations association
with clusters is optimized
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
Latent class analysis for segmenting preferences of investment bonds
Market segmentation is a key component of conjoint analysis which addresses consumer
preference heterogeneity. Members in a segment are assumed to be homogenous in their
views and preferences when worthing an item but distinctly heterogenous to members of other
segments. Latent class methodology is one of the several conjoint segmentation procedures
that overcome the limitations of aggregate analysis and a-priori segmentation. The main
benefit of Latent class models is that market segment membership and regression parameters
of each derived segment are estimated simultaneously. The Latent class model presented in
this paper uses mixtures of multivariate conditional normal distributions to analyze rating
data, where the likelihood is maximized using the EM algorithm. The application focuses on
customer preferences for investment bonds described by four attributes; currency, coupon
rate, redemption term and price. A number of demographic variables are used to generate
segments that are accessible and actionable.peer-reviewe
Using Rasch analysis to form plausible health states amenable to valuation: the development of CORE-6D from CORE-OM in order to elicit preferences for common mental health problems
Purpose: To describe a new approach for deriving a preference-based index from a condition specific measure that uses Rasch analysis to develop health states.
Methods: CORE-OM is a 34-item instrument monitoring clinical outcomes of people with common mental health problems. CORE-OM is characterised by high correlation across its domains. Rasch analysis was used to reduce the number of items and response levels in order to produce a set of unidimensionally-behaving items, and to generate a credible set of health states corresponding to different levels of symptom severity using the Rasch item threshold map.
Results: The proposed methodology resulted in the development of CORE-6D, a 2-dimensional health state description system consisting of a unidimensionally-behaving 5-item emotional component and a physical symptom item. Inspection of the Rasch item threshold map of the emotional component helped identify a set of 11 plausible health states, which, combined with the physical symptom item levels, will be used for the valuation of the instrument, resulting in the development of a preference-based index.
Conclusions: This is a useful new approach to develop preference-based measures where the domains of a measure are characterised by high correlation. The CORE-6D preference-based index will enable calculation of Quality Adjusted Life Years in people with common mental health problems
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