IRJun 14, 2017
Hybrid Collaborative Recommendation via Semi-AutoEncoder
arXiv:1706.04453v248 citations
Originality Synthesis-oriented
AI Analysis
This addresses recommendation system challenges for users and platforms, but it appears incremental as it builds on existing AutoEncoder methods.
The paper tackles the problem of rating prediction and personalized recommendations by introducing a Semi-AutoEncoder structure, achieving state-of-the-art performance on two real-world datasets.
In this paper, we present a novel structure, Semi-AutoEncoder, based on AutoEncoder. We generalize it into a hybrid collaborative filtering model for rating prediction as well as personalized top-n recommendations. Experimental results on two real-world datasets demonstrate its state-of-the-art performances.