Dynamically constructing user profiles with similarity-based online incremental clustering
Source: International Journal of Advanced Intelligence Paradigms, Volume 1, Number 4, 25 June 2009 , pp. 377-397(21)
Publisher: Inderscience Publishers
Abstract:User profiling is a widely used technique to analyse and store user interests and preferences to apply this knowledge to improve user experiences with information systems. In this research paper, we present an approach for dynamically constructing user profiles, particularly from uniform relevance feedback in information-seeking activities. We propose an inference method for user interests, which we call High-Similarity Sequence Data-Driven (H2S2D) clustering and discuss its peculiarities and show its superiority for the creation of high-quality concepts, which are the elementary constituents of user profiles. To reflect the volatility of user interests and emphasise the steadiness of persistent preferences, we adopt recency, frequency and persistency as the three main criteria for multi-layered dynamic profile construction and update.
Document Type: Research article
Affiliations: 1: Networked Information Systems Laboratory, Faculty of Human Sciences, Waseda University, Japan. 2: Networked Information Systems Laboratory, Faculty of Human Sciences, Waseda University, Japan
Publication date: 2009-06-25
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