Generalized second-price (GSP) is currently the dominant auction mechanism used in the sponsored search advertising market. However, despite its tremendous commercial success and theoretical optimality, its effectiven...
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Generalized second-price (GSP) is currently the dominant auction mechanism used in the sponsored search advertising market. However, despite its tremendous commercial success and theoretical optimality, its effectiveness is jeopardized by the severe click frauds conducted by advertisers and third-party publishers and the vicious bidding strategy used by advertisers to exhaust the budget of rivals. In this paper, we analyze the drawbacks of GSP that tolerate or even encourage such negative behaviors (i.e., click fraud and vicious bidding) and propose a dynamic modification of the original GSP mechanism to address these drawbacks. Our modified auction mechanism incorporates budget into slot allocation and payment determination and relates the quality score of an advertisement to the current bid. Our analysis shows that our mechanism can effectively reduce the effects of click fraud and vicious bidding.
An average consensus protocol is proposed for discrete-time double-integrator multi-agent systems with communication noises under fixed topologies. The proposed consensus protocol is composed of two parts: the agent&...
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An average consensus protocol is proposed for discrete-time double-integrator multi-agent systems with communication noises under fixed topologies. The proposed consensus protocol is composed of two parts: the agent's own state feedback and the relative states between the agent and its neighbor agents. Due to the existence of communication noises, the relative states cannot be obtained accurately. To attenuate the noise effect, a time-varying consensus gain a(k) is applied to the relative states in the proposed protocol. Hence, the closed-loop dynamics of multi-agent systems is a linear stochastic difference equation with variable coefficients. Fortunately, the state transition matrix of this stochastic system can be solved, and the dynamical behavior of linear multi-agent systems can therefore be determined. It is proved that the proposed protocol is able to solve the mean square average consensus problem if and only if the topology graph is connected;and the time-varying gain a(k) satisfies the stochastic-approximation type conditions (Omission)。
Wireless sensor networks consist of a large number of sensor nodes that have low power and limited transmission range and can be used in various scenario. The nodes can be deployed in the long and narrow region, such ...
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A robotic dolphin with a pair of 3-DOF flippers, two turning units and a multi-link oscillatory propulsion mechanism is designed. The mechanical, hardware and software designs of the robotic dolphin are given. The fli...
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Biomimetic underwater robots have been paid more and more attention because of high efficiency, high maneuverability and low-noise. The undulating ribbon-fins used by rajiformes and gymnotiformes show better maneuvera...
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This paper proposes a reinforcement learning based tag recommendation algorithm to deal with the data sparseness that affects the performance stability of collaborative filtering algorithms. Our algorithm integrates u...
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How to rationally allocate the limited advertising budget is a critical issue in search auctions. However, due to the heterogeneousness of major search markets in terms of auction mechanisms, ranking algorithms and ad...
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How to rationally allocate the limited advertising budget is a critical issue in search auctions. However, due to the heterogeneousness of major search markets in terms of auction mechanisms, ranking algorithms and advertising structures, it is becoming increasingly difficult for an advertiser to manipulate advertising budget simultaneously across several search markets. In this paper, we establish a novel optimal budget allocation model across search advertising markets, under a finite time horizon. By considering distinctive features of search auctions, we introduce the quality score q and the dynamic advertising effort u to extend the advertising response function to fit budget decision scenarios. We also provide a feasible solution to our model and study some desirable properties: (a) the marginal return is non-increasing with respect to the advertising budget;(b) the optimal budget solution satisfies the condition that the advertising effort u is positively proportional to the product of the change of accumulated revenue in a market ∂V, the change of market share ∂ θ and the advertising elasticity α. Computational experiments are made to evaluate our model and identified properties. Experimental results show that the advertiser with increasing advertising elasticity is suggested to invest more budget in the late stages, but the advertiser with decreasing advertising elasticity should invest more budget in the initial stage, in order to maximize net profits.
With serious advertising budget constraints, advertisers have to adjust their daily budget according to the performance of advertisements in real time. Thus we can leave precious budgets to better opportunities in the...
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With serious advertising budget constraints, advertisers have to adjust their daily budget according to the performance of advertisements in real time. Thus we can leave precious budgets to better opportunities in the future, and avoid the surge of ineffective clicks for unnecessary costs. However, advertisers usually have no sufficient knowledge and time for real-time advertising operations in search auctions. We formulate the budget adjustment problem as a state-action decision process in the reinforcement learning (RL) framework. Considering dynamics of marketing environments and some distinctive features of search auctions, we extend continuous reinforcement learning to fit the budget decision scenarios. The market utility is defined as discounted total clicks to get during the remaining period of an advertising schedule. We conduct experiments to validate and evaluate our strategy of budget adjustment with real world data from search advertising campaigns. Experimental results showed that our strategy outperforms the two other baseline strategies.
Two humanoid robots are used to play table tennis with each other. For each humanoid robot, three cameras and a computer are equipped to form a stereovision system and a monocular vision system. The stereovision syste...
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Two humanoid robots are used to play table tennis with each other. For each humanoid robot, three cameras and a computer are equipped to form a stereovision system and a monocular vision system. The stereovision system consisting of two smart cameras and a computer measures the 3-dimensional position of the table tennis ball. It adopts parallel processing mode in order to realize hundred frames level measurement per second. A high-speed digital camera and the computer compose the monocular vision system, which measures the pose of the robot relative to the table via a color mark attached on the robot. The two smart cameras in each stereovision system are synchronized via I/O signals. The vision systems for the two robots are synchronized by time verification. Experimental results verify the effectiveness of the designed vision system and the proposed methods.
Online advertisers bidding in keyword auctions through Web search engines are experiencing fierce competition. Based on a model of advertisers' rational competitive preference, we propose a novel solution concept ...
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Online advertisers bidding in keyword auctions through Web search engines are experiencing fierce competition. Based on a model of advertisers' rational competitive preference, we propose a novel solution concept called the Upper Bound Nash Equilibrium(UBNE) targeting at modeling the competitive bidding dynamics. UBNE yields the best outcome for search engines, and provides a rational explanation of the bid inflation dynamics on keyword markets.
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