342 research outputs found
A lattice framework for pricing display advertisement options with the stochastic volatility underlying model
Advertisement (abbreviated ad) options are a recent development in online
advertising. Simply, an ad option is a first look contract in which a publisher
or search engine grants an advertiser a right but not obligation to enter into
transactions to purchase impressions or clicks from a specific ad slot at a
pre-specified price on a specific delivery date. Such a structure provides
advertisers with more flexibility of their guaranteed deliveries. The valuation
of ad options is an important topic and previous studies on ad options pricing
have been mostly restricted to the situations where the underlying prices
follow a geometric Brownian motion (GBM). This assumption is reasonable for
sponsored search; however, some studies have also indicated that it is not
valid for display advertising. In this paper, we address this issue by
employing a stochastic volatility (SV) model and discuss a lattice framework to
approximate the proposed SV model in option pricing. Our developments are
validated by experiments with real advertising data: (i) we find that the SV
model has a better fitness over the GBM model; (ii) we validate the proposed
lattice model via two sequential Monte Carlo simulation methods; (iii) we
demonstrate that advertisers are able to flexibly manage their guaranteed
deliveries by using the proposed options, and publishers can have an increased
revenue when some of their inventories are sold via ad options.Comment: Bowei Chen and Jun Wang. A lattice framework for pricing display
advertisement options with the stochastic volatility underlying model.
Electronic Commerce Research and Applications, 2015, Volume 14, Issue 6,
pages 465-479, ISSN: 1567-422
A dynamic pricing model for unifying programmatic guarantee and real-time bidding in display advertising
There are two major ways of selling impressions in display advertising. They
are either sold in spot through auction mechanisms or in advance via guaranteed
contracts. The former has achieved a significant automation via real-time
bidding (RTB); however, the latter is still mainly done over the counter
through direct sales. This paper proposes a mathematical model that allocates
and prices the future impressions between real-time auctions and guaranteed
contracts. Under conventional economic assumptions, our model shows that the
two ways can be seamless combined programmatically and the publisher's revenue
can be maximized via price discrimination and optimal allocation. We consider
advertisers are risk-averse, and they would be willing to purchase guaranteed
impressions if the total costs are less than their private values. We also
consider that an advertiser's purchase behavior can be affected by both the
guaranteed price and the time interval between the purchase time and the
impression delivery date. Our solution suggests an optimal percentage of future
impressions to sell in advance and provides an explicit formula to calculate at
what prices to sell. We find that the optimal guaranteed prices are dynamic and
are non-decreasing over time. We evaluate our method with RTB datasets and find
that the model adopts different strategies in allocation and pricing according
to the level of competition. From the experiments we find that, in a less
competitive market, lower prices of the guaranteed contracts will encourage the
purchase in advance and the revenue gain is mainly contributed by the increased
competition in future RTB. In a highly competitive market, advertisers are more
willing to purchase the guaranteed contracts and thus higher prices are
expected. The revenue gain is largely contributed by the guaranteed selling.Comment: Chen, Bowei and Yuan, Shuai and Wang, Jun (2014) A dynamic pricing
model for unifying programmatic guarantee and real-time bidding in display
advertising. In: The Eighth International Workshop on Data Mining for Online
Advertising, 24 - 27 August 2014, New York Cit
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
Smartphone data usage : downlink and uplink asymmetry
Mobile phone usage has changed significantly over the past few years
and smartphone data usage is still not well understood on a statistically
significant scale. This Letter analyses 2.1 million smartphone usage
data values and explore the current wireless downlink–uplink
demand asymmetry for different time periods and across different
radio access networks. The current data demand over 2G networks
remains largely symmetric with strong temporal variations, whereas
the demand over 3G networks is asymmetric with surprisingly weak
temporal variations is shown here
Construction of retrovirus vector taking MDR1/ACBC1 and its transfection into human placenta derived mesenchymal stem cells
In the study, we used both the methods of perfusion and density gradient centrifugation to isolate and purify mesenchymal stem cells (MSCS) from placenta tissue, and constructed a retroviral vector with multiple drug resistant genes, and the green fluorescent protein (GFP) has been used as an indicative mark. The 293T cell was transfected by the retroviral vector PMX-flag-MDR1-GFP together with its peripheral membrane protein gene. After the infective and replication–defective retrovirus were acquired, we transfected them into human placenta-derived mesenchymal stem cells (HPMSCs). We successfully observed the expression of the reporter gene-GFP by using the green light fluorescence microscope and the p-glycoprotein (P-gp) expressed by exogenous gene MDR1 by Western Blotting. All these facts indicated that the retroviral vector PMX-flag-MDR1-GFP had successfully been transfected into HPMSCs and the exogenous gene multidrug resistance (MDR)1 was detected as normally expressed. The daunorubicin (DNR) pump experiment proved that P-gp of HPMSCs transfected with PMX-flag-MDR1-GFP was of biological activity. The result indicates that MDR1 retroviral vector can transfect the HPMSCs. Not only can the exogenous gene be expressed, but also the expression protein had the biological activity. The conclusion lays a solid foundation of the clinical application of MDR1 genetic therapy.Keywords: Transfect, human placenta-derived mesenchymal stem cells, multidrug resistance (MDR)1 gene
Collaborative Edge Caching: a Meta Reinforcement Learning Approach with Edge Sampling
Current learning-based edge caching schemes usually suffer from dynamic
content popularity, e.g., in the emerging short video platforms, users' request
patterns shift significantly over time and across different edges. An intuitive
solution for a specific local edge cache is to collect more request histories
from other edge caches. However, uniformly merging these request histories may
not perform satisfactorily due to heterogeneous content distributions on
different edges. To solve this problem, we propose a collaborative edge caching
framework. First, we design a meta-learning-based collaborative strategy to
guarantee that the local model can timely meet the continually changing content
popularity. Then, we design an edge sampling method to select more "valuable"
neighbor edges to participate in the local training. To evaluate the proposed
framework, we conduct trace-driven experiments to demonstrate the effectiveness
of our design: it improves the average cache hit rate by up to
(normalized) compared with other baselines.Comment: Published on IEEE International Conference on Multimedia and Expo
2023 (ICME2023
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