2006/10/04 by Juan J. Samper, Pedro Á. Castillo, Samper, Juan J. +5
Computer Science · #Data Management and Algorithms #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Information Retrieval (cs.IR) #Information Retrieval and Search Behavior #Recommender Systems and Techniques
paper · pdf · doi:10.48550/arxiv.cs/0610019
openalex publication_date 2006/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper a new RSS feed ranking method called NectaRSS is introduced. The system recommends information to a user based on his/her 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.