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Ontological user profiling in recommender systems
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AI
DOI：10.1145/963770.963773.png)
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Abstract
En 中文
We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. The overall performance of our ontological recommender systems are also presented and favourably compared to other systems in the literature.
Keywords:
algorithms
measurement
design
experimentation
agent
machine learning
ontology
personalization
recommender systems
user profiling
user modelling
Journal
IF:
9.1
Papers:
1.2K
Citations:
4.7K
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