User-centered profile representation for recommendations across multiple content domains

Article type: Research Article

Authors: Fukazawa, Yusuke^{a; *} | Ota, Jun^a

Affiliations: [a] NTT DOCOMO, Inc., 3‒6 Hikari-no-oka, Yokosuka, Kanagawa, Japan | [b] The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, Japan

Correspondence: [*] Corresponding author. E-mail: [email protected]

Abstract: This paper proposes a content-based recommendation method tuned for multiple content domains. Most content-based recommender systems use content attributes (e.g. description of content and category of content) to represent items and user profiles. This raises the problem that when recommending content from new content domain, the recommendation quality will be poor until the item profile representation is updated through the input of a sufficient quantity of contents in the new content domain. In this paper, to develop an item profile representation that is independent of the contents, we propose a method that uses common categories to represent item profiles; it focuses on the real world activities (tasks) that attract the user's interest. Concretely, we present a light-weight task-model that covers a wide variety of tasks and yields item profiles. We also create a recommendation algorithm by incorporating the proposed profile representation into a statistical SVM(Support Vector Machine) based recommendation algorithm. Finally, we conduct a user test and the results of this test show 9% higher user evaluation scores of content recommendations compared to an existing content-based recommendation algorithm using term-based profile representation. This shows the effectiveness of our user-centered profile representation approach.

DOI: 10.3233/KES-2010-0210

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 15, no. 1, pp. 1-14, 2011