Recommender system for contextual advertising in IPTV scenarios

This paper presents a recommender system for contextual targeted advertisement in Video-on-demand scenarios. The proposal arises from a real case of a Japanese company planning to add advertisement to its On-Demand IPTV services. The advertisements consist of icon or text-based links that may be shown before, during or after the playing of the film the customer has selected to watch. The goal of the company is to maximize the number of times customers follow the links to advertised sites because its revenues depends on this. Since only a small portion of the advertising links can be included in a movie, these must be selected carefully. This work proposes a recommender system for selecting the most appropriate advertisement for a certain customer based on the success the advertisement has had in the past among other customers having similar preferences. The paper describes the proposed method, shows the implementation work done so far and describes the remaining work in order to test it in the real scenario.

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