73,910 research outputs found
Distributed Learning in Multi-Armed Bandit with Multiple Players
We formulate and study a decentralized multi-armed bandit (MAB) problem.
There are M distributed players competing for N independent arms. Each arm,
when played, offers i.i.d. reward according to a distribution with an unknown
parameter. At each time, each player chooses one arm to play without exchanging
observations or any information with other players. Players choosing the same
arm collide, and, depending on the collision model, either no one receives
reward or the colliding players share the reward in an arbitrary way. We show
that the minimum system regret of the decentralized MAB grows with time at the
same logarithmic order as in the centralized counterpart where players act
collectively as a single entity by exchanging observations and making decisions
jointly. A decentralized policy is constructed to achieve this optimal order
while ensuring fairness among players and without assuming any pre-agreement or
information exchange among players. Based on a Time Division Fair Sharing
(TDFS) of the M best arms, the proposed policy is constructed and its order
optimality is proven under a general reward model. Furthermore, the basic
structure of the TDFS policy can be used with any order-optimal single-player
policy to achieve order optimality in the decentralized setting. We also
establish a lower bound on the system regret growth rate for a general class of
decentralized polices, to which the proposed policy belongs. This problem finds
potential applications in cognitive radio networks, multi-channel communication
systems, multi-agent systems, web search and advertising, and social networks.Comment: 31 pages, 8 figures, revised paper submitted to IEEE Transactions on
Signal Processing, April, 2010, the pre-agreement in the decentralized TDFS
policy is eliminated to achieve a complete decentralization among player
Simulating the conflict between reputation and profitability for online rating portals
We simulate the process of possible interactions between a set of competitive services and a set of portals that provide online rating for these services. We argue that to have a profitable business, these portals are forced to have subscribed services that are rated by the portals. To satisfy the subscribing services, we make the assumption that the portals improve the rating of a given service by one unit per transaction that involves payment. In this study we follow the 'what-if' methodology, analysing strategies that a service may choose from to select the best portal for it to subscribe to, and strategies for a portal to accept the subscription such that its reputation loss, in terms of the integrity of its ratings, is minimised. We observe that the behaviour of the simulated agents in accordance to our model is quite natural from the real-would perspective. One conclusion from the simulations is that under reasonable conditions, if most of the services and rating portals in a given industry do not accept a subscription policy similar to the one indicated above, they will lose, respectively, their ratings and reputations, and, moreover the rating portals will have problems in making a profit. Our prediction is that the modern portal-rating based economy sector will eventually evolve into a subscription process similar to the one we suggest in this study, as an alternative to a business model based purely on advertising
Recommended from our members
E-commerce and its impact in logistic management: A state of art
Logistics management is defined as that part of the supply chain process that plans, implements, and controls the efficient, effective flow and storage of goods, services, and related information from the point of origin to the point of consumption in order to meet customers' requirements. Traditional logistical issues may be amplified by an e-commerce venture. Adopting e-commerce may greatly expand the marketplace. The organization needs to be concerned with its ability to deliver its product to potential customers. Realizing the importance of E-commerce in logistic management, an attempt has been made in this paper to review the existing literature with the objective to gain insights into the impact of E-commerce in logistic management. The advantages of the E-commerce in logistic management are offered. Finally, summary of findings and calculations are presented
Inefficiencies in Digital Advertising Markets
Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research
Born to trade: a genetically evolved keyword bidder for sponsored search
In sponsored search auctions, advertisers choose a set of keywords based on products they wish to market. They bid for advertising slots that will be displayed on the search results page when a user submits a query containing the keywords that the advertiser selected. Deciding how much to bid is a real challenge: if the bid is too low with respect to the bids of other advertisers, the ad might not get displayed in a favorable position; a bid that is too high on the other hand might not be profitable either, since the attracted number of conversions might not be enough to compensate for the high cost per click.
In this paper we propose a genetically evolved keyword bidding strategy that decides how much to bid for each query based on historical data such as the position obtained on the previous day. In light of the fact that our approach does not implement any particular expert knowledge on keyword auctions, it did remarkably well in the Trading Agent Competition at IJCAI2009
Online advertising: analysis of privacy threats and protection approaches
Online advertising, the pillar of the âfreeâ content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft
- âŠ