# Application of Knowledge Graphs to Provide Side Information for Improved Recommendation Accuracy

arXiv: https://arxiv.org/abs/2101.03054 · HTML: https://mlsanity.com/paper/2101.03054/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 7 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [DKN: Deep Knowledge-Aware Network for News Recommendation](https://mlsanity.com/paper/1801.08284/) (2018)
- [Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation](https://mlsanity.com/paper/1901.08907/) (2019)
- [Knowledge Graphs on the Web -- an Overview](https://mlsanity.com/paper/2003.00719/) (2020)
- [OK Google, What Is Your Ontology? Or: Exploring Freebase Classification to Understand Google's Knowledge Graph](https://mlsanity.com/paper/1805.03885/) (2018)
- [Discovering Implicational Knowledge in Wikidata](https://mlsanity.com/paper/1902.00916/) (2019)

## Built on by (most cited first, 1 of 1)

- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)

## Abstract (arXiv)

Personalized recommendations are popular in these days of Internet driven activities, specifically shopping. Recommendation methods can be grouped into three major categories, content based filtering, collaborative filtering and machine learning enhanced. Information about products and preferences of different users are primarily used to infer preferences for a specific user. Inadequate information can obviously cause these methods to fail or perform poorly. The more information we provide to these methods, the more likely it is that the methods perform better. Knowledge graphs represent the current trend in recording information in the form of relations between entities, and can provide additional (side) information about products and users. Such information can be used to improve nearest neighbour search, clustering users and products, or train the neural network, when one is used. In this work, we present a new generic recommendation systems framework, that integrates knowledge graphs into the recommendation pipeline. We describe its software design and implementation, and then show through experiments, how such a framework can be specialized for a domain, say movie recommendations, and the improvements in recommendation results possible due to side information obtained from knowledge graphs representation of such information. Our framework supports different knowledge graph representation formats, and facilitates format conversion, merging and information extraction needed for training recommendation methods.