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"Recommendation": models, code, and papers

A Robust Hierarchical Graph Convolutional Network Model for Collaborative Filtering

Apr 30, 2020
Shaowen Peng, Tsunenori Mine

Graph Convolutional Network (GCN) has achieved great success and has been applied in various fields including recommender systems. However, GCN still suffers from many issues such as training difficulties, over-smoothing, vulnerable to adversarial attacks, etc. Distinct from current GCN-based methods which simply employ GCN for recommendation, in this paper we are committed to build a robust GCN model for collaborative filtering. Firstly, we argue that recursively incorporating messages from different order neighborhood mixes distinct node messages indistinguishably, which increases the training difficulty; instead we choose to separately aggregate different order neighbor messages with a simple GCN model which has been shown effective; then we accumulate them together in a hierarchical way without introducing additional model parameters. Secondly, we propose a solution to alleviate over-smoothing by randomly dropping out neighbor messages at each layer, which also well prevents over-fitting and enhances the robustness. Extensive experiments on three real-world datasets demonstrate the effectiveness and robustness of our model.

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Learning Feature Interactions with Lorentzian Factorization Machine

Nov 22, 2019
Canran Xu, Ming Wu

Learning representations for feature interactions to model user behaviors is critical for recommendation system and click-trough rate (CTR) predictions. Recent advances in this area are empowered by deep learning methods which could learn sophisticated feature interactions and achieve the state-of-the-art result in an end-to-end manner. These approaches require large number of training parameters integrated with the low-level representations, and thus are memory and computational inefficient. In this paper, we propose a new model named "LorentzFM" that can learn feature interactions embedded in a hyperbolic space in which the violation of triangle inequality for Lorentz distances is available. To this end, the learned representation is benefited by the peculiar geometric properties of hyperbolic triangles, and result in a significant reduction in the number of parameters (20\% to 80\%) because all the top deep learning layers are not required. With such a lightweight architecture, LorentzFM achieves comparable and even materially better results than the deep learning methods such as DeepFM, xDeepFM and Deep \& Cross in both recommendation and CTR prediction tasks.

* 8 pages, 5 figures, accepted to AAAI-2020 

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Fast Feature Reduction in intrusion detection datasets

Apr 01, 2013
Shafigh Parsazad, Ehsan Saboori, Amin Allahyar

In the most intrusion detection systems (IDS), a system tries to learn characteristics of different type of attacks by analyzing packets that sent or received in network. These packets have a lot of features. But not all of them is required to be analyzed to detect that specific type of attack. Detection speed and computational cost is another vital matter here, because in these types of problems, datasets are very huge regularly. In this paper we tried to propose a very simple and fast feature selection method to eliminate features with no helpful information on them. Result faster learning in process of redundant feature omission. We compared our proposed method with three most successful similarity based feature selection algorithm including Correlation Coefficient, Least Square Regression Error and Maximal Information Compression Index. After that we used recommended features by each of these algorithms in two popular classifiers including: Bayes and KNN classifier to measure the quality of the recommendations. Experimental result shows that although the proposed method can't outperform evaluated algorithms with high differences in accuracy, but in computational cost it has huge superiority over them.

* Parsazad, Shafigh; Saboori, Ehsan; Allahyar, Amin; , "Fast Feature Reduction in intrusion detection datasets," MIPRO, 2012 Proceedings of the 35th International Convention , vol., no., pp.1023-1029, 21-25 May 2012 

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The Benchmark Lottery

Jul 14, 2021
Mostafa Dehghani, Yi Tay, Alexey A. Gritsenko, Zhe Zhao, Neil Houlsby, Fernando Diaz, Donald Metzler, Oriol Vinyals

The world of empirical machine learning (ML) strongly relies on benchmarks in order to determine the relative effectiveness of different algorithms and methods. This paper proposes the notion of "a benchmark lottery" that describes the overall fragility of the ML benchmarking process. The benchmark lottery postulates that many factors, other than fundamental algorithmic superiority, may lead to a method being perceived as superior. On multiple benchmark setups that are prevalent in the ML community, we show that the relative performance of algorithms may be altered significantly simply by choosing different benchmark tasks, highlighting the fragility of the current paradigms and potential fallacious interpretation derived from benchmarking ML methods. Given that every benchmark makes a statement about what it perceives to be important, we argue that this might lead to biased progress in the community. We discuss the implications of the observed phenomena and provide recommendations on mitigating them using multiple machine learning domains and communities as use cases, including natural language processing, computer vision, information retrieval, recommender systems, and reinforcement learning.

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Confidence-Budget Matching for Sequential Budgeted Learning

Feb 05, 2021
Yonathan Efroni, Nadav Merlis, Aadirupa Saha, Shie Mannor

A core element in decision-making under uncertainty is the feedback on the quality of the performed actions. However, in many applications, such feedback is restricted. For example, in recommendation systems, repeatedly asking the user to provide feedback on the quality of recommendations will annoy them. In this work, we formalize decision-making problems with querying budget, where there is a (possibly time-dependent) hard limit on the number of reward queries allowed. Specifically, we consider multi-armed bandits, linear bandits, and reinforcement learning problems. We start by analyzing the performance of `greedy' algorithms that query a reward whenever they can. We show that in fully stochastic settings, doing so performs surprisingly well, but in the presence of any adversity, this might lead to linear regret. To overcome this issue, we propose the Confidence-Budget Matching (CBM) principle that queries rewards when the confidence intervals are wider than the inverse square root of the available budget. We analyze the performance of CBM based algorithms in different settings and show that they perform well in the presence of adversity in the contexts, initial states, and budgets.

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Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks

Aug 28, 2019
Yuting Ye, Xuwu Wang, Jiangchao Yao, Kunyang Jia, Jingren Zhou, Yanghua Xiao, Hongxia Yang

Low-dimensional embeddings of knowledge graphs and behavior graphs have proved remarkably powerful in varieties of tasks, from predicting unobserved edges between entities to content recommendation. The two types of graphs can contain distinct and complementary information for the same entities/nodes. However, previous works focus either on knowledge graph embedding or behavior graph embedding while few works consider both in a unified way. Here we present BEM , a Bayesian framework that incorporates the information from knowledge graphs and behavior graphs. To be more specific, BEM takes as prior the pre-trained embeddings from the knowledge graph, and integrates them with the pre-trained embeddings from the behavior graphs via a Bayesian generative model. BEM is able to mutually refine the embeddings from both sides while preserving their own topological structures. To show the superiority of our method, we conduct a range of experiments on three benchmark datasets: node classification, link prediction, triplet classification on two small datasets related to Freebase, and item recommendation on a large-scale e-commerce dataset.

* 25 pages, 5 figures, 10 tables. CIKM 2019 

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Learning From Weights: A Cost-Sensitive Approach For Ad Retrieval

Dec 03, 2018
Nikit Begwani, Shrutendra Harsola, Rahul Agrawal

Retrieval models such as CLSM is trained on click-through data which treats each clicked query-document pair as equivalent. While training on click-through data is reasonable, this paper argues that it is sub-optimal because of its noisy and long-tail nature (especially for sponsored search). In this paper, we discuss the impact of incorporating or disregarding the long tail pairs in the training set. Also, we propose a weighing based strategy using which we can learn semantic representations for tail pairs without compromising the quality of retrieval. We conducted our experiments on Bing sponsored search and also on Amazon product recommendation to demonstrate that the methodology is domain agnostic. Online A/B testing on live search engine traffic showed improvements in clicks (11.8\% higher CTR) and as well as improvement in quality (8.2\% lower bounce rate) when compared to the unweighted model. We also conduct the experiment on Amazon Product Recommendation data where we see slight improvements in NDCG Scores calculated by retrieving among co-purchased product.

* 7 pages, 5 figures, DAPA, WSDM Workshop 

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Scalable representation learning and retrieval for display advertising

Jan 04, 2021
Olivier Koch, Amine Benhalloum, Guillaume Genthial, Denis Kuzin, Dmitry Parfenchik

Over the past decades, recommendation has become a critical component of many online services such as media streaming and e-commerce. Recent advances in algorithms, evaluation methods and datasets have led to continuous improvements of the state-of-the-art. However, much work remains to be done to make these methods scale to the size of the internet. Online advertising offers a unique testbed for recommendation at scale. Every day, billions of users interact with millions of products in real-time. Systems addressing this scenario must work reliably at scale. We propose an efficient model (LED, for Lightweight Encoder-Decoder) reaching a new trade-off between complexity, scale and performance. Specifically, we show that combining large-scale matrix factorization with lightweight embedding fine-tuning unlocks state-of-the-art performance at scale. We further provide the detailed description of a system architecture and demonstrate its operation over two months at the scale of the internet. Our design allows serving billions of users across hundreds of millions of items in a few milliseconds using standard hardware.

* 12 pages, 9 figures 

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TrimTuner: Efficient Optimization of Machine Learning Jobs in the Cloud via Sub-Sampling

Nov 09, 2020
Pedro Mendes, Maria Casimiro, Paolo Romano, David Garlan

This work introduces TrimTuner, the first system for optimizing machine learning jobs in the cloud to exploit sub-sampling techniques to reduce the cost of the optimization process while keeping into account user-specified constraints. TrimTuner jointly optimizes the cloud and application-specific parameters and, unlike state of the art works for cloud optimization, eschews the need to train the model with the full training set every time a new configuration is sampled. Indeed, by leveraging sub-sampling techniques and data-sets that are up to 60x smaller than the original one, we show that TrimTuner can reduce the cost of the optimization process by up to 50x. Further, TrimTuner speeds-up the recommendation process by 65x with respect to state of the art techniques for hyper-parameter optimization that use sub-sampling techniques. The reasons for this improvement are twofold: i) a novel domain specific heuristic that reduces the number of configurations for which the acquisition function has to be evaluated; ii) the adoption of an ensemble of decision trees that enables boosting the speed of the recommendation process by one additional order of magnitude.

* Mascots 2020 

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Algorithmic Recourse: from Counterfactual Explanations to Interventions

Feb 28, 2020
Amir-Hossein Karimi, Bernhard Schölkopf, Isabel Valera

As machine learning is increasingly used to inform consequential decision-making (e.g., pre-trial bail and loan approval), it becomes important to explain how the system arrived at its decision, and also suggest actions to achieve a favorable decision. Counterfactual explanations -- "how the world would have (had) to be different for a desirable outcome to occur" -- aim to satisfy these criteria. Existing works have primarily focused on designing algorithms to obtain counterfactual explanations for a wide range of settings. However, one of the main objectives of "explanations as a means to help a data-subject act rather than merely understand" has been overlooked. In layman's terms, counterfactual explanations inform an individual where they need to get to, but not how to get there. In this work, we rely on causal reasoning to caution against the use of counterfactual explanations as a recommendable set of actions for recourse. Instead, we propose a shift of paradigm from recourse via nearest counterfactual explanations to recourse through minimal interventions, moving the focus from explanations to recommendations. Finally, we provide the reader with an extensive discussion on how to realistically achieve recourse beyond structural interventions.

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