Enhancing Top-N Item Recommendations by Peer Collaboration

@article{Sun2022EnhancingTI,
  title={Enhancing Top-N Item Recommendations by Peer Collaboration},
  author={Yang Sun and Fajie Yuan and Min Yang and Alexandros Karatzoglou and Shen Li and Xiaoyan Zhao},
  journal={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year={2022}
}
  • Yang Sun, Fajie Yuan, Xiaoyan Zhao
  • Published 31 October 2021
  • Computer Science
  • Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Deep neural networks (DNN) based recommender models often require numerous parameters to achieve remarkable performance. However, this inevitably brings redundant neurons, a phenomenon referred to as over-parameterization. In this paper, we plan to exploit such redundancy phenomena for recommender systems (RS), and propose a top-N item recommendation framework called PCRec that leverages collaborative training of two recommender models of the same network structure, termed peer collaboration… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 46 REFERENCES
A Generic Network Compression Framework for Sequential Recommender Systems
TLDR
A compressed sequential recommendation framework, termed as CpRec, where two generic model shrinking techniques are employed and a block-wise adaptive decomposition to approximate the input and softmax matrices by exploiting the fact that items in SRS obey a long-tailed distribution.
Session-based Recommendations with Recurrent Neural Networks
TLDR
It is argued that by modeling the whole session, more accurate recommendations can be provided by an RNN-based approach for session-based recommendations, and introduced several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem.
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Layer Stacking
TLDR
This work presents StackRec, a simple but very efficient training framework for deep SR models by layer stacking, and proposes progressively stacking such pre-trained residual layers/blocks so as to yield a deeper but easier-to-train SR model.
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking
TLDR
This work proposes the stacking operation on the pre-trained layers/blocks to transfer knowledge from a shallower model to a deep model, then performs iterative stacking so as to yield a much deeper but easier-to-train SR model.
A Simple Convolutional Generative Network for Next Item Recommendation
TLDR
A simple, but very effective generative model that is capable of learning high-level representation from both short- and long-range item dependencies is introduced that attains state-of-the-art accuracy with less training time in the next item recommendation task.
Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation
TLDR
A new encoder-decoder framework named Gap-filling based Recommender (GRec), which trains the encoder and decoder by a gap-fills mechanism and significantly outperforms the state-of-the-art sequential recommendation methods.
Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
TLDR
This paper develops a parameter-efficient transfer learning architecture, termed as PeterRec, which can be configured on-the-fly to various downstream tasks, and shows that PeterRec performs efficient transfer learning in multiple domains, where it achieves comparable or sometimes better performance relative to fine-tuning the entire model parameters.
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
TLDR
This paper shows that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions, and combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture.
Self-Attentive Sequential Recommendation
TLDR
Extensive empirical studies show that the proposed self-attention based sequential model (SASRec) outperforms various state-of-the-art sequential models (including MC/CNN/RNN-based approaches) on both sparse and dense datasets.
DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation
TLDR
Experiments show that DAML achieves significantly better rating prediction accuracy compared to the state-of-the-art methods, and the attention mechanism can highlight the relevant information in reviews to increase the interpretability of rating prediction.
...
...