Long Tail Recommender Utilizing Information Diffusion Theory

Our approach aims to provide a mechanism for recommending long tail items to knowledge workers. The approach employs collaborative filtering using browsing features of identified key population of the diffusion of information. We conducted analytic experiment for a novel recommendation algorithm bas...

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Bibliographische Detailangaben
Hauptverfasser:	Ishikawa, Masayuki, Geczy, Peter, Izumi, Noriaki, Yamaguchi, Takahira
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Applied computing > Operations research > Decision analysis Collaborative filtering Collaborative work Computing methodologies > Artificial intelligence > Knowledge representation and reasoning Information analysis Information Diffusion Information filtering Information filters Information systems > Information systems applications > Data mining Information systems > Information systems applications > Decision support systems Innovator Theory Intelligent agent Knowledge management technology Long Tail Marketing and sales Probability distribution Recommender System Technological innovation Theory of computation > Logic Uniform resource locators
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Beschreibung
Zusammenfassung:	Our approach aims to provide a mechanism for recommending long tail items to knowledge workers. The approach employs collaborative filtering using browsing features of identified key population of the diffusion of information. We conducted analytic experiment for a novel recommendation algorithm based on the browsing features of identified selected users and discovered that the first 10 users accessing a particular page play the key role in information spread. The evaluation indicated that our approach is effective for long tail recommendation.
DOI:	10.1109/WIIAT.2008.352