99 research outputs found
Multi-keyword multi-click advertisement option contracts for sponsored search
In sponsored search, advertisement (abbreviated ad) slots are usually sold by
a search engine to an advertiser through an auction mechanism in which
advertisers bid on keywords. In theory, auction mechanisms have many desirable
economic properties. However, keyword auctions have a number of limitations
including: the uncertainty in payment prices for advertisers; the volatility in
the search engine's revenue; and the weak loyalty between advertiser and search
engine. In this paper we propose a special ad option that alleviates these
problems. In our proposal, an advertiser can purchase an option from a search
engine in advance by paying an upfront fee, known as the option price. He then
has the right, but no obligation, to purchase among the pre-specified set of
keywords at the fixed cost-per-clicks (CPCs) for a specified number of clicks
in a specified period of time. The proposed option is closely related to a
special exotic option in finance that contains multiple underlying assets
(multi-keyword) and is also multi-exercisable (multi-click). This novel
structure has many benefits: advertisers can have reduced uncertainty in
advertising; the search engine can improve the advertisers' loyalty as well as
obtain a stable and increased expected revenue over time. Since the proposed ad
option can be implemented in conjunction with the existing keyword auctions,
the option price and corresponding fixed CPCs must be set such that there is no
arbitrage between the two markets. Option pricing methods are discussed and our
experimental results validate the development. Compared to keyword auctions, a
search engine can have an increased expected revenue by selling an ad option.Comment: Chen, Bowei and Wang, Jun and Cox, Ingemar J. and Kankanhalli, Mohan
S. (2015) Multi-keyword multi-click advertisement option contracts for
sponsored search. ACM Transactions on Intelligent Systems and Technology, 7
(1). pp. 1-29. ISSN: 2157-690
The added value of online user-generated content in traditional methods for influenza surveillance
Abstract There has been considerable work in evaluating the efficacy of using online data for health surveillance. Often comparisons with baseline data involve various squared error and correlation metrics. While useful, these overlook a variety of other factors important to public health bodies considering the adoption of such methods. In this paper, a proposed surveillance system that incorporates models based on recent research efforts is evaluated in terms of its added value for influenza surveillance at Public Health England. The system comprises of two supervised learning approaches trained on influenza-like illness (ILI) rates provided by the Royal College of General Practitioners (RCGP) and produces ILI estimates using Twitter posts or Google search queries. RCGP ILI rates for different age groups and laboratory confirmed cases by influenza type are used to evaluate the models with a particular focus on predicting the onset, overall intensity, peak activity and duration of the 2015/16 influenza season. We show that the Twitter-based models perform poorly and hypothesise that this is mostly due to the sparsity of the data available and a limited training period. Conversely, the Google-based model provides accurate estimates with timeliness of approximately one week and has the potential to complement current surveillance systems
Time-Series Adaptive Estimation of Vaccination Uptake Using Web Search Queries
Estimating vaccination uptake is an integral part of ensuring public health.
It was recently shown that vaccination uptake can be estimated automatically
from web data, instead of slowly collected clinical records or population
surveys. All prior work in this area assumes that features of vaccination
uptake collected from the web are temporally regular. We present the first ever
method to remove this assumption from vaccination uptake estimation: our method
dynamically adapts to temporal fluctuations in time series web data used to
estimate vaccination uptake. We show our method to outperform the state of the
art compared to competitive baselines that use not only web data but also
curated clinical data. This performance improvement is more pronounced for
vaccines whose uptake has been irregular due to negative media attention (HPV-1
and HPV-2), problems in vaccine supply (DiTeKiPol), and targeted at children of
12 years old (whose vaccination is more irregular compared to younger
children)
Seasonal Web Search Query Selection for Influenza-Like Illness (ILI) Estimation
Influenza-like illness (ILI) estimation from web search data is an important
web analytics task. The basic idea is to use the frequencies of queries in web
search logs that are correlated with past ILI activity as features when
estimating current ILI activity. It has been noted that since influenza is
seasonal, this approach can lead to spurious correlations with features/queries
that also exhibit seasonality, but have no relationship with ILI. Spurious
correlations can, in turn, degrade performance. To address this issue, we
propose modeling the seasonal variation in ILI activity and selecting queries
that are correlated with the residual of the seasonal model and the observed
ILI signal. Experimental results show that re-ranking queries obtained by
Google Correlate based on their correlation with the residual strongly favours
ILI-related queries
Neural network models for influenza forecasting with associated uncertainty using Web search activity trends.
Influenza affects millions of people every year. It causes a considerable amount of medical visits and hospitalisations as well as hundreds of thousands of deaths. Forecasting influenza prevalence with good accuracy can significantly help public health agencies to timely react to seasonal or novel strain epidemics. Although significant progress has been made, influenza forecasting remains a challenging modelling task. In this paper, we propose a methodological framework that improves over the state-of-the-art forecasting accuracy of influenza-like illness (ILI) rates in the United States. We achieve this by using Web search activity time series in conjunction with historical ILI rates as observations for training neural network (NN) architectures. The proposed models incorporate Bayesian layers to produce associated uncertainty intervals to their forecast estimates, positioning themselves as legitimate complementary solutions to more conventional approaches. The best performing NN, referred to as the iterative recurrent neural network (IRNN) architecture, reduces mean absolute error by 10.3% and improves skill by 17.1% on average in nowcasting and forecasting tasks across 4 consecutive flu seasons
Tracking COVID-19 using online search
Previous research has demonstrated that various properties of infectious
diseases can be inferred from online search behaviour. In this work we use time
series of online search query frequencies to gain insights about the prevalence
of COVID-19 in multiple countries. We first develop unsupervised modelling
techniques based on associated symptom categories identified by the United
Kingdom's National Health Service and Public Health England. We then attempt to
minimise an expected bias in these signals caused by public interest -- as
opposed to infections -- using the proportion of news media coverage devoted to
COVID-19 as a proxy indicator. Our analysis indicates that models based on
online searches precede the reported confirmed cases and deaths by 16.7 (10.2 -
23.2) and 22.1 (17.4 - 26.9) days, respectively. We also investigate transfer
learning techniques for mapping supervised models from countries where the
spread of disease has progressed extensively to countries that are in earlier
phases of their respective epidemic curves. Furthermore, we compare time series
of online search activity against confirmed COVID-19 cases or deaths jointly
across multiple countries, uncovering interesting querying patterns, including
the finding that rarer symptoms are better predictors than common ones.
Finally, we show that web searches improve the short-term forecasting accuracy
of autoregressive models for COVID-19 deaths. Our work provides evidence that
online search data can be used to develop complementary public health
surveillance methods to help inform the COVID-19 response in conjunction with
more established approaches.Comment: Published in Nature Digital Medicine. Please note that the published
version differs from this preprin
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