WS= \(\{ w_{i} \}^{m}_{i=1}\)

WS is the set of workers, w
_{
i
} is the ith worker, m is the total number of workers

VS= \(\{ v_{j} \}^{n}_{j=1}\)

VS is the set of tasks, v
_{
j
} is the jth 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 kth 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 workertask 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 workercategory 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 taskcategory 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(xμ,σ
^{2})

Probability density function of the Gaussian distribution with mean μ and variance σ
^{2}
