Corpus ID: 235795003

SVP-CF: Selection via Proxy for Collaborative Filtering Data

@article{Sachdeva2021SVPCFSV,
  title={SVP-CF: Selection via Proxy for Collaborative Filtering Data},
  author={Noveen Sachdeva and Carole-Jean Wu and Julian McAuley},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.04984}
}
We study the practical consequences of dataset sampling strategies on the performance of recommendation algorithms. Recommender systems are generally trained and evaluated on samples of larger datasets. Samples are often taken in a naı̈ve or ad-hoc fashion: e.g. by sampling a dataset randomly or by selecting users or items with many interactions. As we demonstrate, commonly-used data sampling schemes can have significant consequences on algorithm performance—masking performance deficiencies in… Expand

Figures and Tables from this paper

References

SHOWING 1-10 OF 36 REFERENCES
On Sampling Strategies for Neural Network-based Collaborative Filtering
TLDR
A general neural network-based recommendation framework is proposed, which subsumes several existing state-of-the-art recommendation algorithms, and the efficiency issue is addressed by investigating sampling strategies in the stochastic gradient descent training for the framework. Expand
Exploring Data Splitting Strategies for the Evaluation of Recommendation Models
TLDR
The results demonstrate that the splitting strategy employed is an important confounding variable that can markedly alter the ranking of recommender systems, making much of the currently published literature non-comparable, even when the same datasets and metrics are used. Expand
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. Expand
BPR: Bayesian Personalized Ranking from Implicit Feedback
TLDR
This paper presents a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem and provides a generic learning algorithm for optimizing models with respect to B PR-Opt. Expand
Are we really making much progress? A worrying analysis of recent neural recommendation approaches
TLDR
A systematic analysis of algorithmic proposals for top-n recommendation tasks that were presented at top-level research conferences in the last years sheds light on a number of potential problems in today's machine learning scholarship and calls for improved scientific practices in this area. Expand
Variational Autoencoders for Collaborative Filtering
TLDR
A generative model with multinomial likelihood and use Bayesian inference for parameter estimation is introduced and the pros and cons of employing a principledBayesian inference approach are identified and characterize settings where it provides the most significant improvements. Expand
Neural Collaborative Filtering
TLDR
This work strives to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback, and presents a general framework named NCF, short for Neural network-based Collaborative Filtering. Expand
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
TLDR
A novel method based on highly efficient random walks to structure the convolutions and a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model are developed. Expand
Off-policy Bandits with Deficient Support
TLDR
This work systematically analyzed the statistical and computational properties of three approaches that provide various guarantees for IPS-based learning despite the inherent limitations of support-deficient data: restricting the action space, reward extrapolation, and restricting the policy space. Expand
Data Mining Methods for Recommender Systems
TLDR
In this chapter, an overview of the main Data Mining techniques used in the context of Recommender Systems is given, including Bayesian Networks and Support Vector Machines. Expand
...
1
2
3
4
...