DNNRec: A novel deep learning based hybrid recommender system

作者：

Highlights：

• A novel hybrid deep learning based recommender system ‘DNNRec’ is proposed.

• DNNRec leverages embeddings, combines side information and a very deep network.

• DNNRec addresses cold start case and learns of non-linear latent factors.

• Proposed solution is benchmarked against existing methods on accuracy and run time.

• DNNRec outperforms state-of-the-art methods overall and in cold start case.

摘要

•A novel hybrid deep learning based recommender system ‘DNNRec’ is proposed.•DNNRec leverages embeddings, combines side information and a very deep network.•DNNRec addresses cold start case and learns of non-linear latent factors.•Proposed solution is benchmarked against existing methods on accuracy and run time.•DNNRec outperforms state-of-the-art methods overall and in cold start case.

论文关键词：Deep learning,Recommender systems,Embeddings,Side information,Cyclical learning rates,Deep neural network,Cold start problem

论文评审过程：Received 17 December 2018, Revised 18 August 2019, Accepted 25 October 2019, Available online 29 October 2019, Version of Record 13 November 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.113054