GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Journal of Machine Learning Research
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Application of kernels to link analysis
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Kernel methods for predicting protein--protein interactions
Bioinformatics
Algorithm 862: MATLAB tensor classes for fast algorithm prototyping
ACM Transactions on Mathematical Software (TOMS)
An Experimental Investigation of Graph Kernels on a Collaborative Recommendation Task
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning multiple graphs for document recommendations
Proceedings of the 17th international conference on World Wide Web
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
MetaFac: community discovery via relational hypergraph factorization
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal recommendation on graphs via long- and short-term preference fusion
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Online egocentric models for citation networks
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We propose a method to predict users' interests in social media, using time-evolving, multinomial relational data. We exploit various actions performed by users, and their preferences to predict user interests. Actions performed by users in social media such as Twitter, Delicious and Facebook have two fundamental properties. (a) User actions can be represented as high-dimensional or multinomial relations - e.g. referring URLs, bookmarking and tagging, clicking a favorite button on a post etc. (b) User actions are time-varying and user-specific - each user has unique preferences that change over time. Consequently, it is appropriate to represent each user's action at some point in time as a multinomial relational data. We propose ActionGraph, a novel graph representation for modeling users' multinomial, time-varying actions. Each user's action at some time point is represented by an action node. ActionGraph is a bipartite graph whose edges connect an action node to its involving entities, referred to as object nodes. Using real-world social media data, we empirically justify the proposed graph structure. Our experimental results show that the proposed ActionGraph improves the accuracy in a user interest prediction task by outperforming several baselines including standard tensor analysis, a previously proposed state-of-the-art LDA-based method and other graph-based variants. Moreover, the proposed method shows robust performances in the presence of sparse data.