An Adaptive User Profile for Filtering News Based on a User Interest Hierarchy
نویسندگان
چکیده
A prototype system for the filtering and ranking of news items has been developed and a pilot test has been conducted. The user’s interests are modeled by a user interest hierarchy based on explicit user feedback with adaptive learning after each session. The system learned very quickly, reaching normalized recall values of over 0.9 within three sessions. When the user’s interests “drifted”, the system adapted but the speed with which it adapted seemed dependent on the amount of feedback provided by the user.
منابع مشابه
A New Similarity Measure Based on Item Proximity and Closeness for Collaborative Filtering Recommendation
Recommender systems utilize information retrieval and machine learning techniques for filtering information and can predict whether a user would like an unseen item. User similarity measurement plays an important role in collaborative filtering based recommender systems. In order to improve accuracy of traditional user based collaborative filtering techniques under new user cold-start problem a...
متن کاملAdaptive User Modeling for Filtering Electronic News
A prototype system for the fine-grained filtering of news items has been developed and a pilot test has been conducted. The system is based on an adaptive user model that integrates stereotypes and artificial neural networks. The stereotypes are based on newspaper sections and sub-sections, along with editor specified and user specified keywords. Eight subjects trained the system over six days ...
متن کاملAdaptive User Profile Model and Collaborative Filtering for Personalized News
In recent years, personalized news recommendation has received increasing attention in IR community. The core problem of personalized recommendation is to model and track users’ interests and their changes. To address this problem, both content-based filtering (CBF) and collaborative filtering (CF) have been explored. User interests involve interests on fixed categories and dynamic events, yet ...
متن کاملA NOVEL FUZZY-BASED SIMILARITY MEASURE FOR COLLABORATIVE FILTERING TO ALLEVIATE THE SPARSITY PROBLEM
Memory-based collaborative filtering is the most popular approach to build recommender systems. Despite its success in many applications, it still suffers from several major limitations, including data sparsity. Sparse data affect the quality of the user similarity measurement and consequently the quality of the recommender system. In this paper, we propose a novel user similarity measure based...
متن کاملEvaluating a User-Model Based Personalisation Architecture for Digital News Services
An architecture that provides personalised filtering and dissemination of news items is presented. It is based on user profiles and it provides mechanisms that allow the user to control and tailor to his own needs the interaction between three different sources of relevance judgements: the existing newspaper categorisation by sections, basic information retrieval on user selected keywords, and ...
متن کامل