Building an Integrated Pan-European News Distribution Network

Schranz, Markus; Dustdar, Schahram; Platzer, Christian

doi:10.1007/0-387-29360-4_62

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…Hence, the maximum expected reward of agent i for context x is π i (x) = π f * i (x) (x). 1,2 Based on this, the set of the best choices of agent i for context x is given as k * i (x) := arg max k∈K i π k (x). On the other hand, the set of the best actions for context x is f * (x) := arg max f ∈F π f (x).…”

Section: Modeling Multiagent Learning Using Cooperative Contextual Bamentioning

confidence: 99%

Multiagent Systems: Learning, Strategic Behavior, Cooperation, and Network Formation

Tekin

Zhang

et al. 2018

Cooperative and Graph Signal Processing

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Proliferation of web-based multimedia content sources and servers led to a tremendous growth in the volume and diversity of multimedia content that is consumed by a diverse set of users. This diversity results in users exhibiting a vast range of preferences over the content, which often depends on the context in which they consume the content. Such demand led to the emergence of multimedia content aggregators (MCAs) [1,2] that gather and fuse content from numerous multimedia sources to provide a ubiquitous content delivery experience for their users. It is thus essential for these systems to learn the context-specific content preferences of their users using past feedback of their users on the content that they provide. The context of a user includes information that is related to its content preferences, including but not limited to the location information, search query, gender, age, and the type of the device that the user is using (e.g., mobile phone, tablet, PC) to access the content [3]. Thus, the goal of the MCA is to match its users with the most appropriate content by learning how users with different contexts react to different contents. Such learning is necessary for continuous satisfaction of a user's request for content, which dynamically evolves over time depending on how the user's context evolves. This problem can be formulated as an online learning problem where the MCA learns the best content for its users through exploring how its users react to different content. In order to maximize the user satisfaction, an MCA needs to connect with other MCAs that have access to other multimedia sources to find the right content for its users. This requires cooperation between the MCAs: In addition to serving its own users, an MCA should also serve content to the users of other MCAs when a request is made. Thus, each MCA has two types of users: direct users, who visit the website of the MCA to search for content, and indirect users, who are users of another MCA Cooperative and Graph Signal Processing.

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Section: Modeling Multiagent Learning Using Cooperative Contextual Bamentioning

confidence: 99%

Multiagent Systems: Learning, Strategic Behavior, Cooperation, and Network Formation

Tekin

Zhang

et al. 2018

Cooperative and Graph Signal Processing

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“…Example 1: Business news aggregation. Consider a distributed set of news aggregators that operate in different countries (for instance a European news aggregator network as in [8]). Each news aggregator's content network (as portrayed in Fig.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Theorem 3: When DISCOM-W is run with parameters 8 where T s is the stability parameter which is given in Assumption 2, the time averaged regret of CA i by time T is…”

Section: Learning Under Dynamic User and Contentmentioning

confidence: 99%

“…Business news aggregators can collect information from a variety of multinational and multilingual sources and make personalized recommendations to specific individuals/companies based on their unique needs (see e.g. [8]). Music aggregators enable matching listeners with music content they enjoy both within the content network of the listeners as well as outside this network.…”

Section: Introductionmentioning

confidence: 99%

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Contextual Online Learning for Multimedia Content Aggregation

Tekin

Schaar

2015

IEEE Trans. Multimedia

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The last decade has witnessed a tremendous growth in the volume as well as the diversity of multimedia content generated by a multitude of sources (news agencies, social media, etc.). Faced with a variety of content choices, consumers are exhibiting diverse preferences for content; their preferences often depend on the context in which they consume content as well as various exogenous events. To satisfy the consumers' demand for such diverse content, multimedia content aggregators (CAs) have emerged which gather content from numerous multimedia sources. A key challenge for such systems is to accurately predict what type of content each of its consumers prefers in a certain context, and adapt these predictions to the evolving consumers' preferences, contexts, and content characteristics. We propose a novel, distributed, online multimedia content aggregation framework, which gathers content generated by multiple heterogeneous producers to fulfill its consumers' demand for content. Since both the multimedia content characteristics and the consumers' preferences and contexts are unknown, the optimal content aggregation strategy is unknown a priori. Our proposed content aggregation algorithm is able to learn online what content to gather and how to match content and users by exploiting similarities between consumer types. We prove bounds for our proposed learning algorithms that guarantee both the accuracy of the predictions as well as the learning speed. Importantly, our algorithms operate efficiently even when feedback from consumers is missing or content and preferences evolve over time. Illustrative results highlight the merits of the proposed content aggregation system in a variety of settings.Index Terms-Content aggregation, distributed online learning, multi-armed bandits, social multimedia.

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“…Recent research work within the EU-funded project NEDINE [10] focuses on the creation and the conception of an intelligent news publishing and distribution network consisting of existing local news agencies that use modern Internet and distributed computing technologies to build up a new kind of multinational and multilingual news distribution service. Research results [1,12,16,21] from the area of network communication have been involved to create a scalable loosely coupled service architecture and artificial intelligence is utilized to identify the most relevant related news articles within the investigated multinational Nedine service. For semantic relationship management, the Vector Space Model [13,14,18] has been thoroughly investigated and applied to provide easy access to related and most relevant business news.…”

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confidence: 99%

Pushing the Quality Level in Networked News Business: Semantic-Based Content Retrieval and Composition in International News Publishing

Schranz¹

2008

SJC

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Electronic publishing exploits numerous possibilities to present or exchange information and to communicate via most current media like the Internet. By utilizing modern Web technologies like Web Services, loosely coupled services, and peer-to-peer networks we describe the integration of an intelligent business news presentation and distribution network. Employing semantics technologies enables the coupling of multinational and multilingual business news data on a scalable international level and thus introduce a service quality that is not achieved by alternative technologies in the news distribution area so far. Architecturally, we identified the loose coupling of existing services as the most feasible way to address multinational and multilingual news presentation and distribution networks. Furthermore we semantically enrich multinational news contents by relating them using AI techniques like the Vector Space Model. Summarizing our experiences we describe the technical integration of semantics and communication technologies in order to create a modern international news network.

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Building an Integrated Pan-European News Distribution Network

Cited by 5 publications

References 4 publications

Multiagent Systems: Learning, Strategic Behavior, Cooperation, and Network Formation

Multiagent Systems: Learning, Strategic Behavior, Cooperation, and Network Formation

Contextual Online Learning for Multimedia Content Aggregation

Pushing the Quality Level in Networked News Business: Semantic-Based Content Retrieval and Composition in International News Publishing

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