Every week, I test an application using a natural language interface or natural language technology and I write an article about my experience with this application.
Last week, I tested Reverb, a free news aggregator for iPad. How Reverb meets challenges of news aggregation?
News Recommander Systems
Before to answer at this question, it is necessary to present briefly the news recommendation problem.
The amount of available data on internet has considerably increased and has led to the problem of information overload. Recommender systems try to tackle this issue by offering personalized suggestions. News recommendation is an application of such systems. “The online reading practice leads to the so-called post-click news recommendation problem: when a user has clicked on a news link and is reading an article, he or she is likely to be interested in other related articles. This is still a typical editor’s task, namely an expert who manually looks for relevant content and builds a recommendation set of links, which will be displayed below or next to the current article. News recommender systems attempt to automate such task. Current strategies can be clustered into 3 main categories, namely (a) collaborative filtering focuses on the similarities between users of a service, thus relying on user pro-files data, (b) content-based recommendation that leverages term-driven information retrieval techniques to compute similarities between items, and (c) knowledge-based recommendation mines external data to enrich item descriptions” [Fossati, Giuliano & Tummarello 2012].
I think the application Reverb uses the strategie (b).
Reverb is personnalized
During the first experience, I added my interests on a Word Wall (screenshot 1) like “Natural Language Processing”, “Semantics, Linguistics”, “Machine Learning” simply by writing them (screenshot 2). Then, Reverb created a tailored feed based on my personal interests.
The Word Wall, a technology developped by Reverb, adapts itself according my use. For example, if I read more articles about Machine Learning, this interest comes on the beginning of the Word Wall.
Reverb is simple
With just a tap on an interest, I can read related articles (screenshot 3). By interest, the articles are organized by chronology and by thematic. For example, in the interest “Natural Language Processing”, Reverb proposes some related interests like “Information Extraction”. It is also possible to accede at related news by an article (screenshot 4).
Personally, I’ll keep this news-reading among my iPad applications because the interface gives me the freedom to discover news quickly with the Word Wall and the articles organisation by interests (i.e. Natural Langage Processing) and sub-interests (i.e. information extraction).
- Keep the links of the articles on the web to access directly at the website of a company for example
- Delete the articles which talk about the same news (see approaches of measuring semantic similarity of texts, for example the Latent Semantic Analysis Landauer, Foltz, & Laham (1998))
- A functionality that summarizes a recurrent news like sport results (see approaches of automatic summarization, Radev, McKeown & Hovy 2002)
Here a video presentation of the application:
What do you think about the app? Don’t hesitate to drop your comments below.