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.