3,430 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
Information is not a Virus, and Other Consequences of Human Cognitive Limits
The many decisions people make about what to pay attention to online shape
the spread of information in online social networks. Due to the constraints of
available time and cognitive resources, the ease of discovery strongly impacts
how people allocate their attention to social media content. As a consequence,
the position of information in an individual's social feed, as well as explicit
social signals about its popularity, determine whether it will be seen, and the
likelihood that it will be shared with followers. Accounting for these
cognitive limits simplifies mechanics of information diffusion in online social
networks and explains puzzling empirical observations: (i) information
generally fails to spread in social media and (ii) highly connected people are
less likely to re-share information. Studies of information diffusion on
different social media platforms reviewed here suggest that the interplay
between human cognitive limits and network structure differentiates the spread
of information from other social contagions, such as the spread of a virus
through a population.Comment: accepted for publication in Future Interne
Computational aspects of voting: a literature survey
Preference aggregation is a topic of study in different fields such as philosophy, mathematics, economics and political science. Recently, computational aspects of preference aggregation have gained especial attention and “computational politics” has emerged as a marked line of research in computer science with a clear concentration on voting protocols. The field of voting systems, rooted in social choice theory, has expanded notably in both depth and breadth in the last few decades. A significant amount of this growth comes from studies concerning the computational aspects of voting systems. This thesis comprehensively reviews the work on voting systems (from a computing perspective) by listing, classifying and comparing the results obtained by different researchers in the field. This survey covers a wide range of new and historical results yet provides a profound commentary on related work as individual studies and in relation to other related work and to the field in general. The deliverables serve as an overview where students and novice researchers in the field can start and also as a depository that can be referred to when searching for specific results. A comprehensive literature survey of the computational aspects of voting is a task that has not been undertaken yet and is initially realized here. Part of this research was dedicated to creating a web-depository that contains material and references related to the topic based on the survey. The purpose was to create a dynamic version of the survey that can be updated with latest findings and as an online practical reference
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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