DEFINITION. (MLaS) Let x ∈ R^n denote the concatenation of item x’s image features I , textual features T and numerical features N and n is the dimension of the concatenated features. The problem of MLaS aims to predict the probability of a user interacting (scan/purchase/glance view) in the physical NAME_1 according to the feature vector x. We pose it as a binary classification task and the goal is to predict 1 (item will be interacted at physical NAME_1) or 0 (item will not be interacted at physical NAME_1). Detect plagiarism in the content: