By Sofia Ceppi, Nicola Gatti, Claudio Iuliano (auth.), Esther David, Valentin Robu, Onn Shehory, Sebastian Stein, Andreas Symeonidis (eds.)
This quantity comprises ten completely refereed and revised papers detailing contemporary advances in learn on designing buying and selling brokers and mechanisms for agent-mediated e-commerce. They have been initially provided on the thirteenth foreign Workshop on Agent-Mediated digital trade (AMEC 2011), collocated with AAMAS 2011 in Taipei, Taiwan, or on the 2011 Workshop on buying and selling Agent layout and research (TADA 2011), collocated with IJCAI 2011 in Barcelona, Spain.
The papers awarded at those workshops illustrate either the intensity and wide diversity of analysis themes during this box. they vary from delivering strategies to open theoretical difficulties in on-line scheduling and bargaining lower than uncertainty, to designing bidding brokers in a large zone of software components, resembling digital trade, offer chain administration, or key-phrase ads, to designing brokers that could effectively mirror real human behaviors in lifelike games.
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Additional info for Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets: AMEC 2011, Taipei, Taiwan, May 2, 2011, and TADA 2011, Barcelona, Spain, July 17, 2011, Revised Selected Papers
It seems that it underestimates the VPC, systematically bidding much lower than what is allowed by the publisher’s reserve scores. It is important to note that these scores for 2010 are much higher than 2009. Jordan describes the effect of the reserve score on publisher’s revenue as well as players’ equilibrium showing that AstonTAC and Schlemazl perform much better than TacTex for these higher reserve scores. To rectify this shortcoming, we have implemented a simpler but more reasonable expectation: we aggregate last three days’ conversions and set 14 of this value as our number of conversions for day d as well as day d + 1.
The results show that incorporating time-delayed features onto the prediction task does improve the overall prediction accuracy when compared with no use of lagged data. A comparison with the published results shows that our method is competitive against state-of-the-art methods used within the top performing agents. In particular, our method outperforms existing methods on 20 day ahead predictions in both the component market and the product market. The prediction category for which our method does worst (“current product”) can be explained by the fact that the regression model computes a point estimate for sales of each product on each day and uses the same price for all queries of the same product on a specific day.
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