SESA: Supervised Explicit Semantic Analysis

TL;DR

SESA introduces a supervised model for semantic analysis that embeds items into an interpretable, concept-based space, improving relevance ranking while enhancing explainability in applications like LinkedIn job profiles.

Contribution

The paper presents SESA, a novel supervised model that embeds items into an explicit, semantically meaningful space, extending ESA with supervised learning for ranking tasks.

Findings

Achieves state-of-the-art relevance ranking results.

Provides interpretable embeddings aligned with known concepts.

Utilizes web-scale collaborative skills data effectively.

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.