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"Recommendation": models, code, and papers

Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network

Jan 02, 2021
Xing Li, Wei Wei, Xiangnan Feng, Zhiming Zheng

Graphs are often used to organize data because of their simple topological structure, and therefore play a key role in machine learning. And it turns out that the low-dimensional embedded representation obtained by graph representation learning are extremely useful in various typical tasks, such as node classification, content recommendation and link prediction. However, the existing methods mostly start from the microstructure (i.e., the edges) in the graph, ignoring the mesoscopic structure (high-order local structure). Here, we propose wGCN -- a novel framework that utilizes random walk to obtain the node-specific mesoscopic structures of the graph, and utilizes these mesoscopic structures to reconstruct the graph And organize the characteristic information of the nodes. Our method can effectively generate node embeddings for previously unseen data, which has been proven in a series of experiments conducted on citation networks and social networks (our method has advantages over baseline methods). We believe that combining high-order local structural information can more efficiently explore the potential of the network, which will greatly improve the learning efficiency of graph neural network and promote the establishment of new learning models.

* 8 pages, 3 figures. arXiv admin note: text overlap with arXiv:2007.15838 

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A Large-scale Open Dataset for Bandit Algorithms

Aug 17, 2020
Yuta Saito, Shunsuke Aihara, Megumi Matsutani, Yusuke Narita

We build and publicize the Open Bandit Dataset and Pipeline to facilitate scalable and reproducible research on bandit algorithms. They are especially suitable for off-policy evaluation (OPE), which attempts to predict the performance of hypothetical algorithms using data generated by a different algorithm. We construct the dataset based on experiments and implementations on a large-scale fashion e-commerce platform, ZOZOTOWN. The data contain the ground-truth about the performance of several bandit policies and enable the fair comparisons of different OPE estimators. We also provide a pipeline to make its implementation easy and consistent. As a proof of concept, we use the dataset and pipeline to implement and evaluate OPE estimators. First, we find that a well-established estimator fails, suggesting that it is critical to choose an appropriate estimator. We then select a well-performing estimator and use it to improve the platform's fashion item recommendation. Our analysis succeeds in finding a counterfactual policy that significantly outperforms the historical ones. Our open data and pipeline will allow researchers and practitioners to easily evaluate and compare their bandit algorithms and OPE estimators with others in a large, real-world setting.


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Temporal Graph Networks for Deep Learning on Dynamic Graphs

Jun 18, 2020
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, Michael Bronstein

Graph Neural Networks (GNNs) have recently become increasingly popular due to their ability to learn complex systems of relations or interactions arising in a broad spectrum of problems ranging from biology and particle physics to social networks and recommendation systems. Despite the plethora of different models for deep learning on graphs, few approaches have been proposed thus far for dealing with graphs that present some sort of dynamic nature (e.g. evolving features or connectivity over time). In this paper, we present Temporal Graph Networks (TGNs), a generic, efficient framework for deep learning on dynamic graphs represented as sequences of timed events. Thanks to a novel combination of memory modules and graph-based operators, TGNs are able to significantly outperform previous approaches being at the same time more computationally efficient. We furthermore show that several previous models for learning on dynamic graphs can be cast as specific instances of our framework. We perform a detailed ablation study of different components of our framework and devise the best configuration that achieves state-of-the-art performance on several transductive and inductive prediction tasks for dynamic graphs.


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Towards Robust and Reproducible Active Learning Using Neural Networks

Feb 21, 2020
Prateek Munjal, Nasir Hayat, Munawar Hayat, Jamshid Sourati, Shadab Khan

Active learning (AL) is a promising ML paradigm that has the potential to parse through large unlabeled data and help reduce annotation cost in domains where labeling entire data can be prohibitive. Recently proposed neural network based AL methods use different heuristics to accomplish this goal. In this study, we show that recent AL methods offer a gain over random baseline under a brittle combination of experimental conditions. We demonstrate that such marginal gains vanish when experimental factors are changed, leading to reproducibility issues and suggesting that AL methods lack robustness. We also observe that with a properly tuned model, which employs recently proposed regularization techniques, the performance significantly improves for all AL methods including the random sampling baseline, and performance differences among the AL methods become negligible. Based on these observations, we suggest a set of experiments that are critical to assess the true effectiveness of an AL method. To facilitate these experiments we also present an open source toolkit. We believe our findings and recommendations will help advance reproducible research in robust AL using neural networks.


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Putting Ridesharing to the Test: Efficient and Scalable Solutions and the Power of Dynamic Vehicle Relocation

Feb 12, 2020
Panayiotis Danassis, Marija Sakota, Aris Filos-Ratsikas, Boi Faltings

We perform a systematic evaluation of a diverse set of algorithms for the ridesharing problem which is, to the best of our knowledge, one of the largest and most comprehensive to date. In particular, we evaluate 12 different algorithms over 12 metrics related to global efficiency, complexity, passenger, driver, and platform incentives. Our evaluation setting is specifically designed to resemble reality as closely as possible. We achieve this by (a) using actual data from the NYC's yellow taxi trip records, both for modeling customer requests, and taxis (b) following closely the pricing model employed by ridesharing platforms and (c) running our simulations to the scale of the actual problem faced by the ridesharing platforms. Our results provide a clear-cut recommendation to ridesharing platforms on which solutions can be employed in practice and demonstrate the large potential for efficiency gains. Moreover, we show that simple, lightweight relocation schemes -- which can be used as independent components to any ridesharing algorithm -- can significantly improve Quality of Service metrics by up to 50%. As a highlight of our findings, we identify a scalable, on-device heuristic that offers an efficient, end-to-end solution for the Dynamic Ridesharing and Fleet Relocation problem.


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Mining User Behaviour from Smartphone data: a literature review

Feb 03, 2020
Valentino Servizi, Francisco C. Pereira, Marie K. Anderson, Otto A. Nielsen

To study users' travel behaviour and travel time between origin and destination, researchers employ travel surveys. Although there is consensus in the field about the potential, after over ten years of research and field experimentation, Smartphone-based travel surveys still did not take off to a large scale. Here, computer intelligence algorithms take the role that operators have in Traditional Travel Surveys; since we train each algorithm on data, performances rest on the data quality, thus on the ground truth. Inaccurate validations affect negatively: labels, algorithms' training, travel diaries precision, and therefore data validation, within a very critical loop. Interestingly, boundaries are proven burdensome to push even for Machine Learning methods. To support optimal investment decisions for practitioners, we expose the drivers they should consider when assessing what they need against what they get. This paper highlights and examines the critical aspects of the underlying research and provides some recommendations: (i) from the device perspective, on the main physical limitations; (ii) from the application perspective, the methodological framework deployed for the automatic generation of travel diaries; (iii)from the ground truth perspective, the relationship between user interaction, methods, and data.


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Mining User Behaviour from Smartphone data, a literature review

Dec 24, 2019
Valentino Servizi, Francisco C. Pereira, Marie K. Anderson, Otto A. Nielsen

To study users' travel behaviour and travel time between origin and destination, researchers employ travel surveys. Although there is consensus in the field about the potential, after over ten years of research and field experimentation, Smartphone-based travel surveys still did not take off to a large scale. Here, computer intelligence algorithms take the role that operators have in Traditional Travel Surveys; since we train each algorithm on data, performances rest on the data quality, thus on the ground truth. Inaccurate validations affect negatively: labels, algorithms' training, travel diaries precision, and therefore data validation, within a very critical loop. Interestingly, boundaries are proven burdensome to push even for Machine Learning methods. To support optimal investment decisions for practitioners, we expose the drivers they should consider when assessing what they need against what they get. This paper highlights and examines the critical aspects of the underlying research and provides some recommendations: (i) from the device perspective, on the main physical limitations; (ii) from the application perspective, the methodological framework deployed for the automatic generation of travel diaries; (iii)from the ground truth perspective, the relationship between user interaction, methods, and data.


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Conversion Rate Prediction via Post-Click Behaviour Modeling

Oct 15, 2019
Hong Wen, Jing Zhang, Yuan Wang, Wentian Bao, Quan Lin, Keping Yang

Effective and efficient recommendation is crucial for modern e-commerce platforms. It consists of two indispensable components named Click-Through Rate (CTR) prediction and Conversion Rate (CVR) prediction, where the latter is an essential factor contributing to the final purchasing volume. Existing methods specifically predict CVR using the clicked and purchased samples, which has limited performance affected by the well-known sample selection bias and data sparsity issues. To address these issues, we propose a novel deep CVR prediction method by considering the post-click behaviors. After grouping deterministic actions together, we construct a novel sequential path, which elaborately depicts the post-click behaviors of users. Based on the path, we define the CVR and several related probabilities including CTR, etc., and devise a deep neural network with multiple targets involved accordingly. It takes advantage of the abundant samples with deterministic labels derived from the post-click actions, leading to a significant improvement of CVR prediction. Extensive experiments on both offline and online settings demonstrate its superiority over representative state-of-the-art methods.

* 11 pages, 7 figures, 8 tables 

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Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories

Sep 19, 2019
Jan-Nico Zaech, Cong Gao, Bastian Bier, Russell Taylor, Andreas Maier, Nassir Navab, Mathias Unberath

Metal artifacts in computed tomography (CT) arise from a mismatch between physics of image formation and idealized assumptions during tomographic reconstruction. These artifacts are particularly strong around metal implants, inhibiting widespread adoption of 3D cone-beam CT (CBCT) despite clear opportunity for intra-operative verification of implant positioning, e.g. in spinal fusion surgery. On synthetic and real data, we demonstrate that much of the artifact can be avoided by acquiring better data for reconstruction in a task-aware and patient-specific manner, and describe the first step towards the envisioned task-aware CBCT protocol. The traditional short-scan CBCT trajectory is planar, with little room for scene-specific adjustment. We extend this trajectory by autonomously adjusting out-of-plane angulation. This enables C-arm source trajectories that are scene-specific in that they avoid acquiring "poor images", characterized by beam hardening, photon starvation, and noise. The recommendation of ideal out-of-plane angulation is performed on-the-fly using a deep convolutional neural network that regresses a detectability-rank derived from imaging physics.

* Accepted for oral presentation at the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2019 

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Blocking Bandits

Jul 27, 2019
Soumya Basu, Rajat Sen, Sujay Sanghavi, Sanjay Shakkottai

We consider a novel stochastic multi-armed bandit setting, where playing an arm makes it unavailable for a fixed number of time slots thereafter. This models situations where reusing an arm too often is undesirable (e.g. making the same product recommendation repeatedly) or infeasible (e.g. compute job scheduling on machines). We show that with prior knowledge of the rewards and delays of all the arms, the problem of optimizing cumulative reward does not admit any pseudo-polynomial time algorithm (in the number of arms) unless randomized exponential time hypothesis is false, by mapping to the PINWHEEL scheduling problem. Subsequently, we show that a simple greedy algorithm that plays the available arm with the highest reward is asymptotically $(1-1/e)$ optimal. When the rewards are unknown, we design a UCB based algorithm which is shown to have $c \log T + o(\log T)$ cumulative regret against the greedy algorithm, leveraging the free exploration of arms due to the unavailability. Finally, when all the delays are equal the problem reduces to Combinatorial Semi-bandits providing us with a lower bound of $c' \log T+ \omega(\log T)$.


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