Browsing by Author "Foss, Jeremy"
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- B2B platform for media content personalisationPublication . Malheiro, Benedita; Foss, Jeremy; Burguillo, Juan CarlosThis paper proposes a novel business model to support media content personalisation: an agent-based business-to-business (B2B) brokerage platform for media content producer and distributor businesses. Distributors aim to provide viewers with a personalised content experience and producers wish to en-sure that their media objects are watched by as many targeted viewers as possible. In this scenario viewers and media objects (main programmes and candidate objects for insertion) have profiles and, in the case of main programme objects, are annotated with placeholders representing personalisation opportunities, i.e., locations for insertion of personalised media objects. The MultiMedia Brokerage (MMB) platform is a multiagent multilayered brokerage composed by agents that act as sellers and buyers of viewer stream timeslots and/or media objects on behalf of the registered businesses. These agents engage in negotiations to select the media objects that best match the current programme and viewer profiles.
- DataTV 2019: 1st International Workshop on Data-Driven Personalisation of TelevisionPublication . Foss, Jeremy; Shirley, Ben; Malheiro, Benedita; Kepplinger, Sara; Nixon, Lyndon; Philipp, Basil; Mezaris, Vasilieos; Ulisses, AlexandreThe first international workshop on Data-driven Personalisation of Television aims to highlight the significantly growing importance of data in the support of new television content consumption experiences. This includes automatic video summarization, dynamic insertion of content into media streams and object based media broadcasting, to serve the recommendation of TV content and personalization in media delivery. The workshop has two keynote talks alongside five paper presentations and several related demos.
- Dynamic Personalisation of Media ContentPublication . Malheiro, Benedita; Foss, Jeremy; Burguillo, Juan Carlos; Peleteiro, Ana; Mikic, Fernando A.Dynamic personalization of media content is the latest challenge for media content producers and distributors. The idea is to adapt in near real time the content of a video stream to the viewer's profile. This concept encompasses any type of context-awareness customisation, expressed preferences and viewer profiling. To achieve this goal we propose a multi tier framework composed of a content production tier, a content distribution tier and a content consumption tier, representing producers, distributors and viewers, plus an artefact brokerage tier, implemented as an agent-based e-brokerage platform, to support the dynamic selection of the content to be inserted in the video stream of each viewer.
- In-Programme Personalization for Broadcast: IPP4BPublication . Foss, Jeremy; Malheiro, Benedita; Shirley, Ben; Kepplinger, Sara; Ulisses, Alexandre; Armstrong, MikeThe IPP4B workshop assembles a group of researchers from academia and industry – BBC R&D, Ericsson and MOG Technologies – to discuss the state of the art and together envisage future directions for in-programme personalisation in broadcasting. The workshop comprises one invited keynote, two invited presentations together with a paper and discussion sessions.
- Personalisation of Networked VideoPublication . Foss, Jeremy; Malheiro, Benedita; Burguillo, Juan CarlosFollowing targeted advertising and product placement, TV and online media needs more personalised methods of engaging viewers by integrating advertising and informational messages into playout content, whether real-time broadcast or on-demand. Future advertising solutions need adaptivity to individuals or on-line groups to respond to the commercial requirements of clients and agencies.
- Personalised Dynamic Viewer Profiling for Streamed DataPublication . Veloso, Bruno; Malheiro, Benedita; Burguillo, Juan Carlos; Foss, Jeremy; Gama, JoãoNowadays, not only the number of multimedia resources available is increasing exponentially, but also the crowd-sourced feedback volunteered by viewers generates huge volumes of ratings, likes, shares and posts/reviews. Since the data size involved surpasses human filtering and searching capabilities, there is the need to create and maintain the profiles of viewers and resources to develop recommendation systems to match viewers with resources. In this paper, we propose a personalised viewer profiling technique which creates individual viewer models dynamically. This technique is based on a novel incremental learning algorithm designed for stream data. The results show that our approach outperforms previous approaches, reducing substantially the prediction errors and, thus, increasing the accuracy of the recommendations.
- Personalised fading for stream dataPublication . Veloso, Bruno; Malheiro, Benedita; Burguillo, Juan Carlos; Foss, JeremyThis paper describes a forgetting technique for the live update of viewer profiles based on individual sliding windows, fading and incremental matrix factorization. The individual sliding window maintains, for each viewer, a queue holding the last n viewer ratings. As new viewer events occur, they are inserted in the viewer queue, by shifting and fading the queue ratings, and the viewer latent model is faded. We explored time, rating-and-position and popularity-based fading techniques, using the latter as the base fading algorithm. This approach attempts to address the problem of dynamic viewer profile updating (volatile preferences) as well as the problem of bounded processing resources (fixed size queues). The results show that our approach outperforms previous approaches, improving the quality of the predictions.
- Personalised placement in networked videoPublication . Foss, Jeremy; Malheiro, Benedita; Burguillo, Juan CarlosPersonalised video can be achieved by inserting objects into a video play-out according to the viewer's profile. Content which has been authored and produced for general broadcast can take on additional commercial service features when personalised either for individual viewers or for groups of viewers participating in entertainment, training, gaming or informational activities. Although several scenarios and use-cases can be envisaged, we are focussed on the application of personalised product placement. Targeted advertising and product placement are currently garnering intense interest in the commercial networked media industries. Personalisation of product placement is a relevant and timely service for next generation online marketing and advertising and for many other revenue generating interactive services. This paper discusses the acquisition and insertion of media objects into a TV video play-out stream where the objects are determined by the profile of the viewer. The technology is based on MPEG-4 standards using object based video and MPEG-7 for metadata. No proprietary technology or protocol is proposed. To trade the objects into the video play-out, a Software-as-a-Service brokerage platform based on intelligent agent technology is adopted. Agencies, libraries and service providers are represented in a commercial negotiation to facilitate the contractual selection and usage of objects to be inserted into the video play-out.
- Product Placement Platform for Personalised AdvertisingPublication . Veloso, Bruno; Malheiro, Benedita; Burguillo, Juan Carlos; Foss, JeremyThis paper proposes an integrated approach to personalised product placement involving advertisers, content distributors and viewers. This problem, which is a current challenge for media content producers and distributors, concerns the adaptation of the media content stream with personalised advertising in near real time. Our approach relies on a brokerage and product negotiation platform to match viewer profiles with products descriptions and relegates the actual product placement, i.e., the product rendering process, to the viewer platform. In this scenario, advertisers create and describe product advertisement objects together with the intended target audience using Moving Picture Experts Group (MPEG) standards and a pre-defined metadata schema, while distributors perform viewer profiling and provide viewers with data streams, including personalised products