Title: SESA: Supervised Explicit Semantic Analysis,
Abstract: In recent years supervised representation learning has provided state of the
art or close to the state of the art results in semantic analysis tasks
including ranking and information retrieval. The core idea is to learn how to
embed items into a latent space such that they optimize a supervised objective
in that latent space. The dimensions of the latent space have no clear
semantics, and this reduces the interpretability of the system. For example, in
personalization models, it is hard to explain why a particular item is ranked
high for a given user profile. We propose a novel model of representation
learning called Supervised Explicit Semantic Analysis (SESA) that is trained in
a supervised fashion to embed items to a set of dimensions with explicit
semantics. The model learns to compare two objects by representing them in this
explicit space, where each dimension corresponds to a concept from a knowledge
base. This work extends Explicit Semantic Analysis (ESA) with a supervised
model for ranking problems. We apply this model to the task of Job-Profile
relevance in LinkedIn in which a set of skills defines our explicit dimensions
of the space. Every profile and job are encoded to this set of skills their
similarity is calculated in this space. We use RNNs to embed text input into
this space. In addition to interpretability, our model makes use of the
web-scale collaborative skills data that is provided by users for each LinkedIn
profile. Our model provides state of the art result while it remains
interpretable.