16,192 research outputs found
Simultaneous Inference of User Representations and Trust
Inferring trust relations between social media users is critical for a number
of applications wherein users seek credible information. The fact that
available trust relations are scarce and skewed makes trust prediction a
challenging task. To the best of our knowledge, this is the first work on
exploring representation learning for trust prediction. We propose an approach
that uses only a small amount of binary user-user trust relations to
simultaneously learn user embeddings and a model to predict trust between user
pairs. We empirically demonstrate that for trust prediction, our approach
outperforms classifier-based approaches which use state-of-the-art
representation learning methods like DeepWalk and LINE as features. We also
conduct experiments which use embeddings pre-trained with DeepWalk and LINE
each as an input to our model, resulting in further performance improvement.
Experiments with a dataset of 356K user pairs show that the proposed
method can obtain an high F-score of 92.65%.Comment: To appear in the proceedings of ASONAM'17. Please cite that versio
Structurally Tractable Uncertain Data
Many data management applications must deal with data which is uncertain,
incomplete, or noisy. However, on existing uncertain data representations, we
cannot tractably perform the important query evaluation tasks of determining
query possibility, certainty, or probability: these problems are hard on
arbitrary uncertain input instances. We thus ask whether we could restrict the
structure of uncertain data so as to guarantee the tractability of exact query
evaluation. We present our tractability results for tree and tree-like
uncertain data, and a vision for probabilistic rule reasoning. We also study
uncertainty about order, proposing a suitable representation, and study
uncertain data conditioned by additional observations.Comment: 11 pages, 1 figure, 1 table. To appear in SIGMOD/PODS PhD Symposium
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