Solving Cold-Start Problem in Large-scale Recommendation Engines: A Deep Learning Approach

TL;DR

This paper introduces a deep learning method that can be integrated with existing collaborative filtering recommendation systems to effectively address the item cold-start problem without altering the core CF algorithms.

Contribution

A novel deep learning approach that enhances CF-based recommendation engines by solving the cold-start problem without modifying their core structure.

Findings

Effective in overcoming item cold-start issues

Maintains high recommendation accuracy

Compatible with any CF-based system

Abstract

Collaborative Filtering (CF) is widely used in large-scale recommendation engines because of its efficiency, accuracy and scalability. However, in practice, the fact that recommendation engines based on CF require interactions between users and items before making recommendations, make it inappropriate for new items which haven't been exposed to the end users to interact with. This is known as the cold-start problem. In this paper we introduce a novel approach which employs deep learning to tackle this problem in any CF based recommendation engine. One of the most important features of the proposed technique is the fact that it can be applied on top of any existing CF based recommendation engine without changing the CF core. We successfully applied this technique to overcome the item cold-start problem in Careerbuilder's CF based recommendation engine. Our experiments show that the proposed technique is very efficient to resolve the cold-start problem while maintaining high accuracy of the CF recommendations.