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Table 1 Basic notations throughout this paper

From: An online-updating algorithm on probabilistic matrix factorization with active learning for task recommendation in crowdsourcing systems

Notation Description
WS= \(\{ w_{i} \}^{m}_{i=1}\) WS is the set of workers, w i is the i-th worker, m is the total number of workers
VS= \(\{ v_{j} \}^{n}_{j=1}\) VS is the set of tasks, v j is the j-th task, n is the total number of tasks
CS= \(\{ c_{k} \}^{o}_{k=1}\) CS is the set of task categories, c k is the k-th task category, o is the total number
  of task categories
\(l \in \mathbb {R}\) l is the number of dimensions of latent feature space
\(W \in \mathbb {R}^{l \times m}\) W is the worker latent feature matrix
\(V \in \mathbb {R}^{l \times n}\) V is the task latent feature matrix
\(C \in \mathbb {R}^{l \times o}\) C is the task category latent feature matrix
R = {r ij }, R is the worker-task preferring matrix, r ij is the extent of the favor of task v j
\(R \in \mathbb {R}^{m \times n}\) for worker w i
U = {u ik }, U is the worker-category preferring matrix, u ik is the extent of worker w i ’s
\(U \in \mathbb {R}^{m \times o}\) preference for category c k
D = {d jk }, D is the task-category grouping matrix, d jk indicates the task category c k that
\(D \in \mathbb {R}^{n \times o}\) task v j belongs to
(i,j)P S PS is the set of indexes where the rating r ij is unknown
N(x|μ,σ 2) Probability density function of the Gaussian distribution with mean μ and variance σ 2