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Access to online contents represents a large share of the Internet traffic. Most such contents are multimedia items which are user-generated, i.e., posted online by the contents owners. In this paper we focus on how those who provide contents can leverage online platforms in order to profit from their large base of potential viewers. Actually, platforms like Vimeo or YouTube provide tools to accelerate the dissemination of contents, i.e., recommendation lists and other re-ranking mechanisms. Hence, the popularity of a content can be increased by paying a cost for advertisement: doing so, it will appear with some priority in the recommendation lists and will be accessed more frequently by the platform users. Ultimately, such acceleration mechanism engenders a competition among online contents to gain popularity. In this context, our focus is on the structure of the acceleration strategies which a content provider should use in order to optimally promote a content given a certain daily budget. Such a best response indeed depends on the strategies adopted by competing content providers. Also, it is a function of the potential popularity of a content and the fee paid for the platform advertisement service. We formulate the problem as a differential game and we solve it for the infinite horizon case by deriving the structure of certain Nash equilibria of the game.
Social media-transmitted online information, particularly content that is emotionally charged, shapes our thoughts and actions. In this study, we incorporate social network theories and analyses to investigate how emotions shape online content diffus
Stochastic differential games have been used extensively to model agents competitions in Finance, for instance, in P2P lending platforms from the Fintech industry, the banking system for systemic risk, and insurance markets. The recently proposed mac
Existing game-theoretic planning methods assume that the robot knows the objective functions of the other agents a priori while, in practical scenarios, this is rarely the case. This paper introduces LUCIDGames, an inverse optimal control algorithm t
This article presents a novel approach for learning low-dimensional distributed representations of users in online social networks. Existing methods rely on the network structure formed by the social relationships among users to extract these represe
Multiplayer Online Battle Arena (MOBA) games have received increasing worldwide popularity recently. In such games, players compete in teams against each other by controlling selected game avatars, each of which is designed with different strengths a