Jeremy Foss

Personalised fading for stream data

This 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 video

Personalised video can be achieved by inserting objects into a video play-out according to the viewers 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.

Dynamic Personalisation of Media Content

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 viewers 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.

Personalised Dynamic Viewer Profiling for Streamed Data

Nowadays, 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.

In-Programme Personalization for Broadcast: IPP4B

The IPP4B workshop assembled 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 comprised one invited keynote, two invited presentations together with a paper and discussion sessions.