# Hybrid Recommender Systems: A Systematic Literature Review

## TL;DR

This paper systematically reviews hybrid recommender systems over the past decade, highlighting common techniques, challenges like cold-start, and future research directions including contextualization and larger datasets.

## Contribution

First comprehensive quantitative review of hybrid recommender systems, analyzing techniques, challenges, and future opportunities in the last decade.

## Key findings

- Most studies combine collaborative filtering with other techniques.
- Cold-start and data sparsity are the main addressed problems.
- Movies datasets are predominantly used in evaluations.

## Abstract

Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. This systematic literature review presents the state of the art in hybrid recommender systems of the last decade. It is the first quantitative review work completely focused in hybrid recommenders. We address the most relevant problems considered and present the associated data mining and recommendation techniques used to overcome them. We also explore the hybridization classes each hybrid recommender belongs to, the application domains, the evaluation process and proposed future research directions. Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way. Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors. As most of the studies are evaluated by comparisons with similar methods using accuracy metrics, providing more credible and user oriented evaluations remains a typical challenge. Besides this, newer challenges were also identified such as responding to the variation of user context, evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid recommenders represent a good basis with which to respond accordingly by exploring newer opportunities such as contextualizing recommendations, involving parallel hybrid algorithms, processing larger datasets, etc.