Representation learning via Dual-Autoencoder for recommendation

By Fuzhen Zhuang, Zhiqiang Zhang, Mingda Qian, Chuan Shi, Xing Xie, Qing He. 2017

107 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, neutral): “A dual-autoencoder architecture [91] (Fig.”
Deep Learning Based Recommender System (2017, unknown): “Autoencoder [34, 88, 89, 114, 116, 126, 137, 138, 141, 160, 178, 188, 208], [4, 10, 32, 94, 151, 152, 159, 171, 172, 189, 197, 209, 210]”
Recommendation system based on deep learning methods: a systematic review and new directions (2019, methodology): “Others include external information by introducing additional regularization (Zhuang et al. 2017; Liang et al. 2018; Suglia et al. 2017), user behavior (Chen et al. 2018), contextual information (Smirnova 2017) and auxiliary information (He et al. 2017) such as knowledge base and temporal signals.”
A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “decoder part [27], [28], [29], [30], [52], [53], [54].”
A Survey on Knowledge Graph-Based Recommender Systems (2020, background): “One common implementation is the latent factor model [55], [56], which extracts the latent representation of the user and item from the high dimensional user-item interaction matrix, and then computes the similarity between the user and item with the inner product or other methods.”