New paper available online: NectaRSS, an intelligent RSS feed reader

After a few comings and goings (not very many, the submission process and the JNCA staff were very helpful), the paper NectaRSS, an intelligent RSS feed reader has been accepted at the Journal of Network and Computer Applications. Here’s the abstract:

In this paper a novel article ranking method called NectaRSS is introduced. The system recommends incoming articles, which we will designate as newsitems, to users based on their past choices. User preferences are automatically acquired, avoiding explicit feedback, and ranking is based on those preferences distilled to a user profile. NectaRSS uses the well-known vector space model for user profiles and new documents, and compares them using information retrieval techniques, but introduces a novel method for user profile creation and adaptation from users’ past choices. The efficiency of the proposed method has been tested by embedding it into an intelligent aggregator (RSS feed reader), which has been used by different and heterogeneous users. Besides, this paper proves that the ranking of newsitems yielded by NectaRSS improves its quality with user’s choices, and its superiority over other algorithms that use a different information representation method.

This article is a result of the PhD of Juan José Samper. And we (eventually) plan to implement it in an online aggregator.

Large-Scale Newscast Computing on the Internet,

The last paper commented on the Friday paper seminar, which just came back after the holidays, was Large-Scale Newscast Computing on the Internet, by Jelasity et al.; this paper describes a so-called gossip protocol for maintaining an overlay network and disseminating information over it. The protocol is really simple: each node keeps a cache, and interchanges it upon being contacted by other nodes. This keeps the number of connections small, and creates a small-world network, allowing for easy and fast dissemination of information.
We have been using this kind of protocol for evolutionary computation, and will keep doing so in our current grants. All in all, an useful and interesting paper.