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Found 21 papers, 8 papers with code
Improved Estimation of Ranks for Learning Item Recommenders with Negative Sampling
In this work, we demonstrate the benefits from correcting the bias introduced by sampling of negatives.
Private Learning with Public Features
We study a class of private learning problems in which the data is a join of private and public features.
Answering Compositional Queries with Set-Theoretic Embeddings
The need to compactly and robustly represent item-attribute relations arises in many important tasks, such as faceted browsing and recommendation systems.
ALX: Large Scale Matrix Factorization on TPUs
We present ALX, an open-source library for distributed matrix factorization using Alternating Least Squares, written in JAX.
iALS++: Speeding up Matrix Factorization with Subspace Optimization
However, iALS does not scale well with large embedding dimensions, d, due to its cubic runtime dependency on d. Coordinate descent variations, iCD, have been proposed to lower the complexity to quadratic in d. In this work, we show that iCD approaches are not well suited for modern processors and can be an order of magnitude slower than a careful iALS implementation for small to mid scale embedding sizes (d ~ 100) and only perform better than iALS on large embeddings d ~ 1000.
Revisiting the Performance of iALS on Item Recommendation Benchmarks
Matrix factorization learned by implicit alternating least squares (iALS) is a popular baseline in recommender system research publications.
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
We study the problem of differentially private (DP) matrix completion under user-level privacy.
Item Recommendation from Implicit Feedback
Core challenges of item recommendation are (1) how to formulate a training objective from implicit feedback and (2) how to efficiently train models over a large item catalogue.
Rankmax: An Adaptive Projection Alternative to the Softmax Function
Several machine learning models involve mapping a score vector to a probability vector.
Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval
In light of these problems, we observed that most online content platforms have both a search and a recommender system that, while having heterogeneous input spaces, can be connected through their common output item space and a shared semantic representation.
Neural Collaborative Filtering vs. Matrix Factorization Revisited
This approach is often referred to as neural collaborative filtering (NCF).
Ranked #6 on
Link Prediction
on Yelp
Superbloom: Bloom filter meets Transformer
We extend the idea of word pieces in natural language models to machine learning tasks on opaque ids.
Evaluation Metrics for Item Recommendation under Sampling
The task of item recommendation requires ranking a large catalogue of items given a context.
On the Difficulty of Evaluating Baselines: A Study on Recommender Systems
Numerical evaluations with comparisons to baselines play a central role when judging research in recommender systems.
Ranked #1 on
Recommendation Systems
on MovieLens 10M
Efficient Training on Very Large Corpora via Gramian Estimation
We study the problem of learning similarity functions over very large corpora using neural network embedding models.
Adaptive Sampled Softmax with Kernel Based Sampling
We empirically study the trade-off of bias, sampling distribution and sample size and show that kernel based sampling results in low bias with few samples.
Graph Based Relational Features for Collective Classification
Statistical Relational Learning (SRL) methods have shown that classification accuracy can be improved by integrating relations between samples.
A Generic Coordinate Descent Framework for Learning from Implicit Feedback
A diversity of complex models has been proposed for a wide variety of applications.
BPR: Bayesian Personalized Ranking from Implicit Feedback
In this paper we present a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem.
Factorization Machines
So unlike nonlinear SVMs, a transformation in the dual form is not necessary and the model parameters can be estimated directly without the need of any support vector in the solution.
Factorization Machines
So unlike nonlinear SVMs, a transformation in the dual form is not necessary and the model parameters can be estimated directly without the need of any support vector in the solution.
