# Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios

arXiv: https://arxiv.org/abs/1706.07956 · HTML: https://mlsanity.com/paper/1706.07956/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2017.

## Built on by (most cited first, 4 of 4)

- [Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces](https://mlsanity.com/paper/2211.03536/) (2022)
- [A Scalable Recommendation Engine for New Users and Items](https://mlsanity.com/paper/2209.06128/) (2022)
- [Review of Explainable Graph-Based Recommender Systems](https://mlsanity.com/paper/2408.00166/) (2024)
- [Multi-Scale Feature and Metric Learning for Relation Extraction](https://mlsanity.com/paper/2107.13425/) (2021)

## Abstract (arXiv)

In the last decade, driven also by the availability of an unprecedented computational power and storage capabilities in cloud environments we assisted to the proliferation of new algorithms, methods, and approaches in two areas of artificial intelligence: knowledge representation and machine learning. On the one side, the generation of a high rate of structured data on the Web led to the creation and publication of the so-called knowledge graphs. On the other side, deep learning emerged as one of the most promising approaches in the generation and training of models that can be applied to a wide variety of application fields. More recently, autoencoders have proven their strength in various scenarios, playing a fundamental role in unsupervised learning. In this paper, we instigate how to exploit the semantic information encoded in a knowledge graph to build connections between units in a Neural Network, thus leading to a new method, SEM-AUTO, to extract and weigh semantic features that can eventually be used to build a recommender system. As adding content-based side information may mitigate the cold user problems, we tested how our approach behave in the presence of a few rating from a user on the Movielens 1M dataset and compare results with BPRSLIM.