Walid Krichene
Walid Krichene received his Ph.D. in Electrical Engineering and Computer Sciences from the University of California at Berkeley. His research focuses on optimization theory and differential privacy, and their applications to recommendation systems. He received the M.S. in Applied Mathematics from the Ecole des Mines Paristech and the M.A. in Mathematics from the University of California, Berkeley. He previously worked at Criteo labs, the CDC department at Caltech, and the CAOR group at the Ecole des Mines. Walid received the Best Paper Award at KDD 2020, the Leon Chua Award for outstanding achievement in nonlinear science, the Valedictorian Prize for the Tunisian Baccalaureate, and a Bronze Medal at the Pan African Math Olympiads.
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Revisiting the Performance of iALS on Item Recommendation Benchmarks
Steffen Rendle
Li Zhang
Yehuda Koren
Proceedings of the 16th ACM Conference on Recommender Systems, Association for Computing Machinery(2022), pp. 427-435
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Matrix factorization learned by implicit alternating least squares (iALS) is a popular baseline in recommender system research publications. iALS is known to be one of the most computationally efficient and scalable collaborative filtering methods. However, recent studies suggest that its prediction quality is not competitive with the current state of the art, in particular autoencoders and other item-based collaborative filtering methods. In this work, we revisit four well-studied benchmarks where iALS was reported to perform poorly and show that with proper tuning, iALS is highly competitive and outperforms any method on at least half of the comparisons. We hope that these high quality results together with iALS's known scalability spark new interest in applying and further improving this decade old technique.
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Private Alternating Least Squares: (Nearly) OptimalPrivacy/Utility Trade-off for Matrix Completion
Abhradeep Guha Thakurta
Li Zhang
Steffen Rendle
NA, NA(2021), NA
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We study the problem of differentially private (DP) matrix completion under user-level privacy. We design an $(\epsilon,\delta)$-joint differentially private variant of the popular Alternating-Least-Squares (ALS) method that achieves: i) (nearly) optimal sample complexity for matrix completion (in terms of number of items, users), and ii) best known privacy/utility trade-off both theoretically, as well as on benchmark data sets.
In particular, despite non-convexity of low-rank matrix completion and ALS, we provide the first global convergence analysis of ALS with {\em noise} introduced to ensure DP. For $n$ being the number of users and $m$ being the number of items in the rating matrix, our analysis requires only about $\log (n+m)$ samples per user (ignoring rank, condition number factors) and obtains a sample complexity of $n=\tilde\Omega(m/(\sqrt{\zeta}\cdot \epsilon))$ to ensure relative Frobenius norm error of $\zeta$. This improves significantly on the previous state of the result of $n=\tilde\Omega\left(m^{5/4}/(\zeta^{5}\epsilon)\right)$ for the private-FW method by ~\citet{jain2018differentially}.
Furthermore, we extensively validate our method on synthetic and benchmark data sets (MovieLens 10mi, MovieLens 20mi), and observe that private ALS only suffers a 6 percentage drop in accuracy when compared to the non-private baseline for $\epsilon\leq 10$. Furthermore, compared to prior work of~\cite{jain2018differentially}, it is at least better by 10 percentage for all choice of the privacy parameters.
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We extend the idea of word pieces in natural language models to machine learning tasks on opaque ids. This is achieved by applying hash functions to map each id to multiple hash tokens in a much smaller space, similarly to a Bloom filter. We show that by applying a multi-layer Transformer to these Bloom filter digests, we are able to obtain models with high accuracy. They outperform models of a similar size without hashing and, to a large degree, models of a much larger size trained using sampled softmax with the same computational budget. Our key observation is that it is important to use a multi-layer Transformer for Bloom filter digests to remove ambiguity in the hashed input. We believe this provides an alternative method to solving problems with large vocabulary size.
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Embedding based models have been the state of the art in collaborative filtering for over a decade. Traditionally, the dot product or higher order equivalents have been used to combine two or more embeddings, e.g., most notably in matrix factorization. In recent years, it was suggested to replace the dot product with a learned similarity e.g. using a multilayer perceptron (MLP). This approach is often referred to as neural collaborative filtering (NCF). In this work, we revisit the experiments of the NCF paper that popularized learned similarities using MLPs. First, we show that with a proper hyperparameter selection, a simple dot product substantially outperforms the proposed learned similarities. Second, while a MLP can in theory approximate any function, we show that it is non-trivial to learn a dot product with an MLP. Finally, we discuss practical issues that arise when applying MLP based similarities and show that MLPs are too costly to use for item recommendation in production environments while dot products allow to apply very efficient retrieval algorithms. We conclude that MLPs should be used with care as embedding combiner and that dot products might be a better default choice.
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Several machine learning models involve mapping a score vector to a probability vector. Usually, this is done by projecting the score vector onto a probability simplex, and such projections are often characterized as Lipschitz continuous approximations of the argmax function, whose Lipschitz constant is controlled by a parameter that is similar to a softmax temperature. The aforementioned parameter has been observed to affect the quality of these models and is typically either treated as a constant or decayed over time. In this work, we propose a method that adapts this parameter to individual training examples. The resulting method exhibits desirable properties, such as sparsity of its support and numerically efficient implementation, and we find that it significantly outperforms competing non-adaptive projection methods. In our analysis, we also derive the general solution of (Bregman) projections onto the (n, k)-simplex, a result which may be of independent interest.
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The task of item recommendation requires ranking a large catalogue of items given a context. Item recommendation algorithms are evaluated using ranking metrics that depend on the positions of relevant items. To speed up the computation of metrics, recent work often uses sampled metrics where only a smaller set of random items and the relevant items are ranked. This paper investigates sampled metrics in more detail and shows that they are inconsistent with their exact version, in the sense that they do not persist relative statements, e.g., recommender A is better than B, not even in expectation. Moreover, the smaller the sampling size, the less difference there is between metrics, and for very small sampling size, all metrics collapse to the AUC metric. We show that it is possible to improve the quality of the sampled metrics by applying a correction, obtained by minimizing different criteria such as bias or mean squared error. We conclude with an empirical evaluation of the naive sampled metrics and their corrected variants. To summarize, our work suggests that sampling should be avoided for metric calculation, however if an experimental study needs to sample, the proposed corrections can improve the quality of the estimate.
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Recent results have shown that for two-layer fully connected neural networks, gradient flow converges to a global optimum in the infinite width limit, by making a connection between the mean field dynamics and the Wasserstein gradient flow. These results were derived for first-order gradient flow, and a natural question is whether second-order dynamics, i.e., dynamics with momentum, exhibit a similar guarantee. We show that the answer is positive for the heavy ball method. In this case, the resulting integro-PDE is a nonlinear kinetic Fokker Planck equation, and unlike the first-order case, it has no apparent connection with the Wasserstein gradient flow. Instead, we study the variations of a Lyapunov functional along the solution trajectories to characterize the stationary points and to prove convergence. While our results are asymptotic in the mean field limit, numerical simulations indicate that global convergence may already occur for reasonably small networks.
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Efficient Training on Very Large Corpora via Gramian Estimation
Nicolas Mayoraz
Steffen Rendle
Li Zhang
Lichan Hong
John Anderson
ICLR 2019 (to appear)
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We study the problem of learning similarity functions over very large corpora using neural network embedding models. These models are typically trained using SGD with random sampling of unobserved pairs, with a sample size that grows quadratically with the corpus size, making it expensive to scale.
We propose new efficient methods to train these models without having to sample unobserved pairs. Inspired by matrix factorization, our approach relies on adding a global quadratic penalty and expressing this term as the inner-product of two generalized Gramians. We show that the gradient of this term can be efficiently computed by maintaining estimates of the Gramians, and develop variance reduction schemes to improve the quality of the estimates. We conduct large-scale experiments that show a significant improvement both in training time and generalization performance compared to sampling methods.
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Randomized Fractal Expansions for Production-Scale Public Collaborative-Filtering Data Sets
Francois Belletti
John Anderson
Karthik Singaram Lakshmanan
Nicolas Mayoraz
Pankaj Kanwar
Taylor Robie
Tayo Oguntebi
Yi-fan Chen
Arxiv(2019)
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Recommender system research suffers from a disconnect between the size of academic data sets and the scale of industrial production systems.
In order to bridge that gap, we propose to generate large-scale user/item interaction data sets by expanding pre-existing public data sets.
Our key contribution is a technique that expands user/item incidence matrices to large numbers of rows (users), columns (items), and non-zero values (interactions). The proposed method adapts Kronecker Graph Theory to preserve key higher order statistical properties such as the fat-tailed distribution of user engagements, item popularity, and singular value spectra of user/item interaction matrices.
Preserving such properties is key to building large realistic synthetic data sets which can be employed reliably to benchmark recommender systems and the systems employed to train them.
We further apply our stochastic expansion algorithm to the binarized MovieLens 20M data set, which comprises 20M interactions between 27K movies and 138K users.
The resulting expanded data set has 1.2B ratings, 2.2M users, and 855K items, which can be scaled up or down.
Furthermore, we present collaborative filtering experiments demonstrating that the generated synthetic data entails valuable insights for machine learning at scale in recommender systems.
We provide code pointers to reproduce our data and our experiments.
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Scaling Up Collaborative Filtering Data Sets through Randomized Fractal Expansions
Francois Belletti
Karthik Singaram Lakshmanan
Nicolas Mayoraz
Yi-fan Chen
John Anderson
Taylor Robie
Tayo Oguntebi
Amit Bleiwess
Dan Shirron
arXiv(2019)
Preview abstract
Recommender System research suffers from a disconnect between the size of academic data sets and the scale of industrial production systems.
In order to bridge that gap, we propose to generate large-scale User/Item interaction data sets by expanding pre-existing public data sets.
Our key contribution is a technique that expands User/Item incidence matrices matrices to large numbers of rows (users), columns (items), and non-zero values (interactions). The proposed method adapts Kronecker Graph Theory to preserve key higher order statistical properties such as the fat-tailed distribution of user engagements, item popularity, and singular value spectra of user/item interaction matrices.
Preserving such properties is key to building large realistic synthetic data sets which in turn can be employed reliably to benchmark Recommender Systems and the systems employed to train them. We further apply our stochastic expansion algorithm to the binarized MovieLens 20M data set, which comprises 20M interactions between 27K movies and 138K users.
The resulting expanded data set has 1.2B ratings, 2.2M users, and 855K items, which can be scaled up or down.
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