Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Fab: content-based, collaborative recommendation
Communications of the ACM
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
IEEE Transactions on Knowledge and Data Engineering
An MDP-Based Recommender System
The Journal of Machine Learning Research
Usage patterns of collaborative tagging systems
Journal of Information Science
Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations
ICML '06 Proceedings of the 23rd international conference on Machine learning
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Information flow modeling based on diffusion rate for prediction and ranking
Proceedings of the 16th international conference on World Wide Web
Towards effective browsing of large scale social annotations
Proceedings of the 16th international conference on World Wide Web
Modeling user behavior in recommender systems based on maximum entropy
Proceedings of the 16th international conference on World Wide Web
Applying collaborative filtering techniques to movie search for better ranking and browsing
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Bookmark hierarchies and collaborative recommendation
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
ItemRank: a random-walk based scoring algorithm for recommender engines
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Web search personalization via social bookmarking and tagging
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
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We propose an algorithm to predict users' future bookmarking using social bookmarking data. It is a problem that primitive collaborative filtering cannot exactly catch users' preferences in social bookmarkings containing enormous items (URLs) because in many cases user's adoption data is sparse. There can be various influences on bookmarking such as effects from the environment and changes in user preference. We use temporal sequence among the bookmarking-users to represent word-of-mouth and among the bookmarked-URLs to represent user's interest, and model each sequential order as a continuous-time Markov chain. This idea comes from diffusion of innovation theory. A transition probability from a state (user/URL) to another state is defined by the transition rate calculated from the time taken for the transition. We predicted user's preferences through a combination of estimating the most likely transition between users using URLs as input and between URLs using users as input. We conducted evaluation experiments with a social bookmarking service in Japan called Hatena bookmark. The proposed algorithm predicts users' preferences with higher accuracy than collaborative filtering or simple transition models based on either user or URL.