Outer Product-based Neural Collaborative Filtering

By Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, Tat-seng Chua. International Joint Conference on Artificial Intelligence, 2018

361 citations recorded by the source corpus.

In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the pairwise correlations between the dimensions of the embedding space. In contrast to existing neural recommender models that combine user embedding and item embedding via a simple concatenation or element-wise product, our proposal of using outer product above the embedding layer results in a two-dimensional interaction map that is more expressive and semantically plausible. Above the interaction map obtained by outer product, we propose to employ a convolutional neural network to learn high-order correlations among embedding dimensions. Extensive experiments on two public implicit feedback data demonstrate the effectiveness of our proposed ONCF framework, in particular, the positive effect of using outer product to model the correlations between embedding dimensions in the low level of multi-layer neural recommender model.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/9cbf32e778128e1e07e309ec644e1654de9fa98b

How researchers see this paper

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

In Artificial Intelligence

1. Xiangnan He
2. Yongfeng Zhang
3. Xu Chen
4. Xiang Wang
5. Wayne Xin Zhao
6. Tat-seng Chua
7. Meng Wang
8. Fuli Feng

In Computer Vision and Pattern Recognition

1. Xiangnan He
2. Xiang Wang
3. Yongfeng Zhang
4. Xu Chen
5. Fuli Feng
6. Meng Wang
7. Yi Tay
8. Le Wu

In Information Systems

1. Xiangnan He
2. Yongfeng Zhang
3. Xu Chen
4. Xiang Wang
5. Tat-seng Chua
6. Meng Wang
7. Wayne Xin Zhao
8. Fuli Feng

Citations in the corpus

• Generate Natural Language Explanations for Recommendation (2021, methodology): “Later approaches to CF more and more advanced to more accurate but less transparent latent factor approaches, beginning from various matrix factorization algorithms [14, 24, 32, 34], to more recent deep learning and neural modeling approaches [9, 10, 37, 42, 43, 46].”
• Deep Learning Based Recommender System (2017, methodology): “der [34, 88, 89, 114, 116, 125, 136, 137, 140, 159, 177, 187, 207], [4, 10, 32, 94, 150, 151, 158, 170, 171, 188, 196, 208, 209] CNNs [25, 49, 50, 75, 76, 98, 105, 127, 130, 153, 165, 172, 202, 206], [6, 44, 51, 83, 110, 126, 143, 148, 169, 190, 191] RNNs [5, 28, 35, 56, 57, 73, 78, 90, 117, 132, 13”
• RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms (2020, background): “(2018) DSSM Huang et al. (2013) FM Rendle (2010) DeepFM Guo et al.”
• Jointly Learning Explainable Rules for Recommendation with Knowledge Graph (2019, methodology): “Following previous studies [5, 13],MLP and GMF are taken as baseline models too.”
• Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis (2018, neutral): “Several recent attempts [10, 16] replace MLP with more complex operations (e.”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, positive): “This kind of models first generate interaction maps via outer product of user and item embeddings, explicitly capturing the pairwise correlations between embedding dimensions [50], [51].”
• Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System (2020, methodology): “For instance, LightGCN [17] and NGCF [43] employ graph convolution to perform matching feature learning, ONCF [18] adopts convolutional layers as the scoring function.”