21,455 research outputs found
Towards a Unified Knowledge-Based Approach to Modality Choice
This paper advances a unified knowledge-based approach to the process of choosing the most appropriate modality or combination of modalities in multimodal output generation. We propose a Modality Ontology (MO) that models the knowledge needed to support the two most fundamental processes determining modality choice â modality allocation (choosing the modality or set of modalities that can best support a particular type of information) and modality combination (selecting an optimal final combination of modalities). In the proposed ontology we model the main levels which collectively determine the characteristics of each modality and the specific relationships between different modalities that are important for multi-modal meaning making. This ontology aims to support the automatic selection of modalities and combinations of modalities that are suitable to convey the meaning of the intended message
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
Optimizing Your Online-Advertisement Asynchronously
We consider the problem of designing optimal online-ad investment strategies
for a single advertiser, who invests at multiple sponsored search sites
simultaneously, with the objective of maximizing his average revenue subject to
the advertising budget constraint. A greedy online investment scheme is
developed to achieve an average revenue that can be pushed to within
of the optimal, for any , with a tradeoff that the
temporal budget violation is . Different from many existing
algorithms, our scheme allows the advertiser to \emph{asynchronously} update
his investments on each search engine site, hence applies to systems where the
timescales of action update intervals are heterogeneous for different sites. We
also quantify the impact of inaccurate estimation of the system dynamics and
show that the algorithm is robust against imperfect system knowledge
Optimal advert placement slot â using the knapsack problem model
The Knapsack problem model is a general resource allocation model in which a single resource is
assigned to a number of alternatives with the objective of maximizing the total return. In this work,
we applied the knapsack problem model to the placement of advert slots in the media. The aim was
to optimize the capital allocated for advert placements. The general practice is that funds are
allocated by trial and error and at the discretions of persons. This approach most times do not yield
maximum results, lesser audience are reached. But when the scientific Knapsack problem model
was applied to industry data, a better result was achieved, wider audience and minimal cost was
attained
Scheduling commercial advertisements for television
The problem of scheduling the commercial advertisements in the television industry is investigated. Each advertiser client demands that the multiple airings of the same brand advertisement should be as spaced as possible over a given time period. Moreover, audience rating requests have to be taken into account in the scheduling. This is the first time this hard decision problem is dealt with in the literature. We design two mixed integer linear programming (MILP) models. Two constructive heuristics, local search procedures and simulated annealing (SA) approaches are also proposed. Extensive computational experiments, using several instances of various sizes, are performed. The results show that the proposed MILP model which represents the problem as a network flow obtains a larger number of optimal solutions and the best non-exact procedure is the one that uses SA
Do a Firm's Equity Returns Reflect the Risk of Its Pension Plan?
This paper examines the empirical question of whether systematic equity risk of U.S. firms as measured by beta from the Capital Asset Pricing Model reflects the risk of their pension plans. There are a number of reasons to suspect that it might not. Chief among them is the opaque set of accounting rules used to report pension assets, liabilities, and expenses. Pension plan assets and liabilities are off-balance sheet, and are often viewed as segregated from the rest of the firm, with its own trustees. Pension accounting rules are complicated. Furthermore, the role of Pension Benefit Guaranty Corporation further clouds the real relation between pension plan risk and firm equity risk. The empirical findings in this paper are consistent with the hypothesis that equity risk does reflect the risk of the firm's pension plan despite arcane accounting rules for pensions. This finding is consistent with informational efficiency of the capital markets. It also has implications for corporate finance practice in the determination of the cost of capital for capital budgeting. Standard procedure uses de-leveraged equity return betas to infer the cost of capital for operating assets. But the de-leveraged betas are not adjusted for the risk of the pension assets and liabilities. Failure to make this adjustment will typically bias upwards estimates of the discount rate for capital budgeting. The magnitude of the bias is shown here to be large for a number of well-known U.S. companies. This bias can result in positive net-present-value projects being rejected.
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
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