Abstract
Most user focused data mining techniques involve purchase pattern analysis, targeted at strictly-formatted database-like transaction records. Most personalization systems employ explicitly provided user preferences rather than implicit rating data obtained automatically by collecting users’ interactions. In this paper, we show that in complex information systems such as digital libraries, implicit rating data can help to characterize users’ research and learning interests, and can be used to cluster users into meaningful groups. Thus, in our personalized recommender system based on collaborative filtering, we employ a user tracking system and a user modeling technique to capture and store users’ implicit ratings. Also, we describe the effects (on community finding) of using four different types of implicit rating data.
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Kim, S., Murthy, U., Ahuja, K., Vasile, S., Fox, E.A. (2005). Effectiveness of Implicit Rating Data on Characterizing Users in Complex Information Systems. In: Rauber, A., Christodoulakis, S., Tjoa, A.M. (eds) Research and Advanced Technology for Digital Libraries. ECDL 2005. Lecture Notes in Computer Science, vol 3652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551362_17
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DOI: https://doi.org/10.1007/11551362_17
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