Discover the final version of the CPN technology bricks!
/Our latest report details the final iteration of the technical infrastructure that provides the features and services of the CPN platform.
The third and final iteration of the “technology bricks” set-up of the CPN platform is now ready, and the details about it are available in our latest report. The technology bricks consist of the components and APIs included in the CPN platform, and have been designed and developed in an iterative manner, gradually adding new features and services that satisfy the user and technical requirements.
The technology bricks developed by the CPN project are classified into three categories: Content, Users and Mapping. Read on to learn more about the different technology bricks.
Content technology bricks
Fine Grained Entity Recognition Module: The goal of the fine-grained entity recognition module is information extraction on news articles. Within the CPN project, the extracted facts (fine-grained named entities, as well as relations among them) are available as structured features for enhancing the content-based part of the recommendation engine. In addition, they can support a better navigation through the data or be used for visualization purposes.
Topic Extractor: This module extracts topics from articles in order to have a concise representation of the content of the article. It performs a language detection in order to apply the most suitable Natural Language Processing techniques to extract terminological candidates (topics). Topics are filtered and finally ranked according to a TF/IDF score, and the article, enriched with these topics, is saved into its final storage (Apache SOLR) to be retrieved later during the recommendation phase. This component is not exposed through the API gateway, but it helps the recommender module answer the question: What topics is the user interested in?
Recommender AB-Testing: The AB-Testing interacts with two modules of the CPN platform: the Recommender module and the API Gateway that is needed to publicly expose this internal API to publishers. It allows every publisher to create recommenders and user groups. It is a “configuration module”, and recommenders and groups can be created and modified in any moment by publisher/administrators of the platform. This module allows news publishers to experiment with different recommendation approaches, collect feedback and formulate insights.
User technology bricks
User modelling: The purpose of this module is to create, maintain and continuously update features associated to users and news items, thus building a profile of the users of the CPN Reader’s App. It also lays down the basis for the recommendation module to work. The module is able to extract metadata from three different source languages, English, Dutch and Greek.
Reader’s app: This is essentially the CPN mobile application which can be used for entering the news environment.
In the main layout, the “Your News” stream includes the personalized news information provided by the Recommender. The “Headlines” stream includes the most important news, characterized as such by the specific source media. The “Popular” stream includes the most read articles from among the media source’s available list of articles.
Within the registration layout, the user enters among others, the preferred media source (non-configurable) and the location of interests. The user can also connect the CPN ID to their Twitter account. This information, together with the locations of interest are sent via CPN API to the Recommender. Moreover, the user has the control of their permissions (location, preferences, time usage). The permissions can be updated at any time, and the user is informed about any updates by means of Personal Data Receipts Brick. For the registration/login process, users can also use their Facebook or Google accounts.
The third version of this module also includes a prototype of a smart speaker application! In Article Details, the user can say:
“read” or “read article” in order to have the title and the content of the article read by the device and its text-to-speech engine.
“interesting” or “mark interesting” to mark the article interesting. After that, the article can be found in the corresponding list of interesting articles.
“irrelevant” or “mark irrelevant” to mark the article irrelevant. After that the article can be found in the corresponding list of irrelevant articles.
At the Article Lists section, the user can say: “top your news” or “top headlines” or “top most read” to get the first five articles of the corresponding stream.
Personal Data Receipt: This module serves as a confirmation to the user that their data is being handled correctly and according to their permissions. It sends them an email receipt (the Personal Data Receipt) whenever they make changes to the permissions they have granted to the system. The module uses blockchain technology to manage transactions on personal user data, and serves to guarantee the rights of the users, as prescribed in the GDPR. (Read more about this feature here.)
Mapping technology bricks
Distribution framework: Current licensing and ownership models in journalism require lengthy negotiations to access, redistribute, and remix content. In the age of the 24h news cycle this can make it difficult to source quality content from freelancers in a timely and secure manner, while also ensuring provenance.
The Distribution Framework aims to simplify the contractual negotiations between creators and editors by creating a pool of licensed content collaboratively managed by multiple trusted journalistic organizations without a single overarching authority. This is achieved by using Distributed Ledger Technology, in particular Hyperledger Fabric, to manage the article licenses.
The Framework sits between all participants in the system, preventing disagreement and simplifying the process of sourcing and distributing news content. In the future this could be extended with pricing and payments to create a trustless distributed content marketplace.
Producer’s App: The Producer’s Dashboard UI, providing analytics on the data collected, and allowing for an easy integration into the producer’s workflow.
The third version of this prototype also allows to set an article as “breaking news”. It also provides contract templates to allow freelancers to easily work together and with editors, to define and track the scope of individual contributions and expected revenues.
Recommender: A core module that computes the most suitable news recommendations for CPN users. It analyses the users’ profiles and collected news to find the most “interesting” news items to be proposed by the app.
This module is built with a hybrid approach that uses variable proportions of content based and collaborative filtering techniques for learning from explicit and implicit feedback given by the users themselves: clicks, ratings, sharing, etc. The system is customizable for including content-delivery strategies’ optimization: multichannel and date/time optimization (predicting the probability of interests at a given time on a given channel) and includes mechanisms for fostering “serendipitous” discoveries.
Pilot 3
The technology bricks, with all their exciting new features, will be tested in a pilot environment starting soon. Follow our website and Twitter account for the latest developments, and stay tuned for the results!
