Financial organizations collect a huge amount of temporal (sequential) data about clients, which is typically collected from multiple sources (modalities). Despite the urgent practical need, developing deep learning techniques suitable to handle such data is limited by the absence of large open-source multi-source real-world datasets of event sequences. To fill this gap, which is mainly caused by security reasons, we present the first industrial-scale publicly available multimodal banking dataset, MBD, that contains information on more than 2M corporate clients of a large bank. Clients are represented by several data sources: 950M bank transactions, 1B geo position events, 5M embeddings of dialogues with technical support, and monthly aggregated purchases of four bank products. All entries are properly anonymized from real proprietary bank data, and the experiments confirm that our anonymization still saves all significant information for introduced downstream tasks. MBD enables supports campaigning task (predict future customer purchases). We provide numerical results for the state-of-the-art event sequence modeling techniques demonstrate the superiority of fusion baselines over single-modal techniques for this task. HuggingFace Link: https://huggingface.co/datasets/ai-lab/MBD Github Link: https://github.com/Dzhambo/MBD

The domain of event sequences is widely applied in various industrial tasks in banking, healthcare, etc., where temporal tabular data processing is required. This paper introduces PyTorch-Lifestream, the first open-source library specially designed to handle event sequences. It supports scenarios with multimodal data and offers a variety of techniques for learning embeddings of event sequences and end-to-end model training. Furthermore, PyTorch-Lifestream efficiently implements state-of-the-art methods for event sequence analysis and adapts approaches from similar domains, thus enhancing the versatility and performance of sequence-based models for a wide range of applications, including financial risk scoring, campaigning, user ID matching, churn prediction, fraud detection, medical diagnostics, and recommender systems.

Gradient Boosting is a leading learning method that builds ensembles and adapts their sizes to particular tasks, consistently delivering top-tier results across various applications. However, determining the optimal number of models in the ensemble remains a critical yet underexplored aspect. Traditional approaches assume a universal ensemble size effective for all data points, which may not always hold true due to data heterogeneity.

Within the realm of Computer-Aided Design (CAD), Boundary-Representation (B-Rep) is the standard option for modeling shapes. We present SpelsNet, a neural architecture for the segmentation of 3D point clouds into surface primitive elements under topological supervision of its B-Rep graph structure. We also propose a point-to-BRep adjacency representation that allows for adapting conventional Linear Algebraic Representation of B-Rep graph structure to the point cloud domain. Thanks to this representation, SpelsNet learns from both spatial and topological domains to enable accurate and topologically consistent surface primitive element segmentation. In particular, SpelsNet is composed of two main components; (1) a supervised 3D spatial segmentation head that outputs B-Rep element types and memberships; (2) a graph-based head that leverages the proposed topological supervision. To enable the learning of SpelsNet with the proposed point-to-BRep adjacency supervision, we extend two existing CAD datasets with the required annotations, and conduct a thorough experimental validation on them. The obtained results showcase the efficacy of SpelsNet and its topological supervision compared to a set of baselines and state-of-the-art approaches.

3D reverse engineering is a long sought-after, yet not completely achieved goal in the Computer-Aided Design (CAD) industry. The objective is to recover the construction history of a CAD model. Starting from a Boundary Representation (B-Rep) of a CAD model, this paper proposes a new deep neural network, CADOps-Net, that jointly learns the CAD operation types and the decomposition into different CAD operation steps. This joint learning allows to divide a B-Rep into parts that were created by various types of CAD operations at the same construction step; therefore providing relevant information for further recovery of the design history. Furthermore, we propose the novel CC3D-Ops dataset that includes over 37k CAD models annotated with CAD operation type labels and step labels. Compared to existing datasets, the complexity and variety of CC3D-Ops models are closer to those used for industrial purposes. Our experiments, conducted on the proposed CC3D-Ops and the publicly available Fusion360 datasets, demonstrate the competitive performance of CADOps-Net with respect to state-of-the-art, and confirm the importance of the joint learning of CAD operation types and steps.

Artem Vazhentsev, Gleb Kuzmin, Artem Shelmanov, Akim Tsvigun, Evgenii Tsymbalov, Kirill Fedyanin, Maxim Panov, Alexander Panchenko, Gleb Gusev, Mikhail Burtsev, Manvel Avetisian, Leonid Zhukov. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

We address the problem of self-supervised learning on discrete event sequences generated by real-world users. Self-supervised learning incorporates complex information from the raw data in low-dimensional fixed-length vector representations that could be easily applied in various downstream machine learning tasks. In this paper, we propose a new method "CoLES", which adapts contrastive learning, previously used for audio and computer vision domains, to the discrete event sequences domain in a self-supervised setting.

Machine learning models using transaction records as inputs are popular among financial institutions. The most efficient models use deep-learning architectures similar to those in the NLP community, posing a challenge due to their tremendous number of parameters and limited robustness. In particular, deep-learning models are vulnerable to adversarial attacks: a little change in the input harms the model's output. In this work, we examine adversarial attacks on transaction records data and defenses from these attacks. The transaction records data have a different structure than the canonical NLP or time-series data, as neighboring records are less connected than words in sentences, and each record consists of both discrete merchant code and continuous transaction amount. We consider a black-box attack scenario, where the attack doesn't know the true decision model and pay special attention to adding transaction tokens to the end of a sequence. These limitations provide a more realistic scenario, previously unexplored in the NLP world. The proposed adversarial attacks and the respective defenses demonstrate remarkable performance using relevant datasets from the financial industry. Our results show that a couple of generated transactions are sufficient to fool a deep-learning model. Further, we improve model robustness via adversarial training or separate adversarial examples detection. This work shows that embedding protection from adversarial attacks improves model robustness, allowing a wider adoption of deep models for transaction records in banking and finance.

This work proposes a novel 3D Deformation Signature (3DS) to represent a 3D deformation signal for 3D Dynamic Face Recognition. 3DS is computed given a non-linear 6D-space representation which guarantees physically plausible 3D deformations. A unique deformation indicator is computed per triangle in a triangulated mesh as a ratio derived from scale and in-plane deformation in the canonical space. These indicators, concatenated, construct the 3DS for each temporal instance. There is a pressing need of non-intrusive bio-metric measurements in domains like surveillance and security. By construction, 3DS is a non-intrusive facial measurement that is resistant to common security attacks like presentation, template and adversarial attacks. Two dynamic datasets (BU4DFE and COMA) were examined, in a standard classification framework, to evaluate 3DS. A first rank recognition accuracy of 99.9%, that outperforms existing literature, was achieved. Assuming an open-world setting, 99.97% accuracy was attained in detecting unseen distractors.

We propose a Point-Voxel DeConvolution (PVDeConv) module for 3D data autoencoder. To demonstrate its efficiency we learn to synthesize high-resolution point clouds of 10k points that densely describe the underlying geometry of Computer Aided Design (CAD) models. Scanning artifacts, such as protrusions, missing parts, smoothed edges and holes, inevitably appear in real 3D scans of fabricated CAD objects. Learning the original CAD model construction from a 3D scan requires a ground truth to be available together with the corresponding 3D scan of an object. To solve the gap, we introduce a new dedicated dataset, the CC3D, containing 50k+ pairs of CAD models and their corresponding 3D meshes. This dataset is used to learn a convolutional autoencoder for point clouds sampled from the pairs of 3D scans - CAD models. The challenges of this new dataset are demonstrated in comparison with other generative point cloud sampling models trained on ShapeNet. The CC3D autoencoder is efficient with respect to memory consumption and training time as compared to stateof-the-art models for 3D data generation.

This paper proposes a novel compact and memory efficient Sparse 3D Deformation Signature (S3DS) to represent a sparse 3D deformation signal for 3D Dynamic Face Recognition. S3DS is based on a non-linear 6D-space representation that secures physically plausible 3D deformations. A unique deformation indicator is computed per triangle in a triangulated mesh, thanks to a recent 3D Deformation Signature (3DS) that is based on Lie Bodies. The proposed S3DS sparsely concatenates unique triangular indicators to construct the facial signature for each temporal instance. The novel descriptor shall benefit domains like surveillance and security in providing non-intrusive bio-metric measurements. By construction, S3DS is resistant to common security attacks like presentation, template and adversarial attacks. Two dynamic datasets (BU4DFE and COMA) were examined in various sparse concatenation settings. Using high reduction rates of $\approx 500$, a first rank recognition accuracy similar to the state of the art was achieved. At low reduction rates of $\approx 40$, S3DS outperformed most existing literature on BU4DFE achieving 99.92%. On COMA, it achieved 99.93% which outperforms existing literature. In an open-world experimental setup, using thousands of distractors, the accuracy reached up to 100% in detecting unseen distractors with high reduction rates in the 3D facial descriptor size.

Internet companies use crowdsourcing to collect large amounts of data needed for creating products based on machine learning techniques. A significant source of such labels for OCR data sets is (re)CAPTCHA, which distinguishes humans from automated bots by asking them to recognize text and, at the same time, receives new labeled data in this way. An important component of such approach to data collection is the reduction of noisy labels produced by bots and non-qualified users.

This paper proposes BODYFITR, a fully automatic method to fit a human body model to static 3D scans with complex poses. Automatic and reliable 3D human body fitting is necessary for many applications related to healthcare, digital ergonomics, avatar creation and security, especially in industrial contexts for large-scale product design. Existing works either make prior assumptions on the pose, require manual annotation of the data or have difficulty handling complex poses. This work addresses these limitations by providing a novel automatic fitting pipeline with carefully integrated building blocks designed for a systematic and robust approach. It is validated on the 3DBodyTex dataset, with hundreds of high-quality 3D body scans, and shown to outperform prior works in static body pose and shape estimation, qualitatively and quantitatively. The method is also applied to the creation of realistic 3D avatars from the high-quality texture scans of 3DBodyTex, further demonstrating its capabilities.

In this paper, we formulate a novel problem of learning to select a set of items maximizing the quality of their ordered list, where the order is predefined by some explicit rule. Unlike the classic information retrieval problem, in our setting, the predefined order of items in the list may not correspond to their quality in general. For example, this is a dominant scenario in personalized news and social media feeds, where items are ordered by publication time in a user interface. We propose new theoretically grounded algorithms based on direct optimization of the resulting list quality. Our offline and online experiments with a large-scale product search engine demonstrate the overwhelming advantage of our methods over the baselines in terms of all key quality metrics.

Stochastic Gradient Boosting (SGB) is a widely used approach to regularization of boosting models based on decision trees. It was shown that, in many cases, random sampling at each iteration can lead to better generalization performance of the model and can also decrease the learning time. Different sampling approaches were proposed, where probabilities are not uniform, and it is not currently clear which approach is the most effective. In this paper, we formulate the problem of randomization in SGB in terms of optimization of sampling probabilities to maximize the estimation accuracy of split scoring used to train decision trees.This optimization problem has a closed-form nearly optimal solution, and it leads to a new sampling technique, which we call Minimal Variance Sampling (MVS).The method both decreases the number of examples needed for each iteration of boosting and increases the quality of the model significantly as compared to the state-of-the art sampling methods. The superiority of the algorithm was confirmed by introducing MVS as a new default option for subsampling in CatBoost, a gradient boosting library achieving state-of-the-art quality on various machine learning tasks.

We present you a program of a balanced mix between an overview of academic achievements in the field of online evaluation and a portion of unique industrial practical experience shared by both the leading researchers and engineers from global Internet companies. First, we give basic knowledge from mathematical statistics. This is followed by foundations of main evaluation methods such as A/B testing, interleaving, and observational studies. Then, we share rich industrial experiences on constructing of an experimentation pipeline and evaluation metrics (emphasizing best practices and common pitfalls). A large part of our tutorial is devoted to modern and state-of-the-art techniques (including the ones based on machine learning) that allow to conduct online experimentation efficiently. We invite software engineers, designers, analysts, and managers of web services and software products, as well as beginners, advanced specialists, and researchers to learn how to make web service development effectively data-driven.

In this paper, a dataset, named 3DBodyTex, of static 3D body scans with high-quality texture information is presented along with a fully automatic method for body model fitting to a 3D scan. 3D shape modelling is a fundamental area of computer vision that has a wide range of applications in the industry. It is becoming even more important as 3D sensing technologies are entering consumer devices such as smartphones. As the main output of these sensors is the 3D shape, many methods rely on this information alone. The 3D shape information is, however, very high dimensional and leads to models that must handle many degrees of freedom from limited information. Coupling texture and 3D shape alleviates this burden, as the texture of 3D objects is complementary to their shape. Unfortunately, high-quality texture content is lacking from commonly available datasets, and in particular in datasets of 3D body scans. The proposed 3DBodyTex dataset aims to fill this gap with hundreds of high-quality 3D body scans with high-resolution texture. Moreover, a novel fully automatic pipeline to fit a body model to a 3D scan is proposed. It includes a robust 3D landmark estimator that takes advantage of the high-resolution texture of 3DBodyTex. The pipeline is applied to the scans, and the results are reported and discussed, showcasing the diversity of the features in the dataset.

This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit. Their combination leads to CatBoost outperforming other publicly available boosting implementations in terms of quality on a variety of datasets. Two critical algorithmic advances introduced in CatBoost are the implementation of ordered boosting, a permutation-driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features. Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms. In this paper, we provide a detailed analysis of this problem and demonstrate that proposed algorithms solve it effectively, leading to excellent empirical results.

Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in "word2vec" software, is usually optimized by stochastic gradient descent.However, the optimization of SGNS objective can be viewed as a problem of searching for a good matrix with the low-rank constraint.The most standard way to solve this type of problems is to apply Riemannian optimization framework to optimize the SGNS objective over the manifold of required low-rank matrices.In this paper, we propose an algorithm that optimizes SGNS objective using Riemannian optimization and demonstrates its superiority over popular competitors, such as the original method to train SGNS and SVD over SPPMI matrix.

State-of-the-art user engagement metrics (such as session-per-user) are widely used by modern Internet companies to evaluate ongoing updates of their web services via A/B testing. These metrics are predictive of companies' long-term goals, but suffer from this property due to slow user learning of an evaluated treatment, which causes a delay in the treatment effect. That, in turn, causes low sensitivity of the metrics and requires to conduct A/B experiments with longer duration or larger set of users from a limited traffic. In this paper, we study how the delay property of user learning can be used to improve sensitivity of several popular metrics of user loyalty and activity. We consider both novel and previously known modifications of these metrics, including different methods of quantifying a trend in a metric's time series and delaying its calculation. These modifications are analyzed with respect to their sensitivity and directionality on a large set of A/B tests run on real users of Yandex. We discover that mostly loyalty metrics gain profit from the considered modifications. We find such modifications that both increase sensitivity of the source metric and are consistent with the sign of its average treatment effect as well.

Nowadays, billions of people use the Web in connection with their daily needs. A significant part of these needs are constituted by search tasks that are usually addressed by search engines. Thus, daily search needs result in regular user engagement with a search engine. User engagement with web services was studied in various aspects, but there appears to be little work devoted to its regularity and periodicity. In this article, we study periodicity of user engagement with a popular search engine through applying spectrum analysis to temporal sequences of different engagement metrics. First, we found periodicity patterns of user engagement and revealed classes of users whose periodicity patterns do not change over a long period of time. In addition, we give an exhaustive analysis of the stability and quality of identified clusters. Second, we used the spectrum series as key metrics to evaluate search quality. We found that the novel periodicity metrics outperform the state-of-the-art quality metrics both in terms of significance level ( p -value) and sensitivity to a large set of larges-scale A/B experiments conducted on real search engine users.

Cold start problem in Collaborative Filtering can be solved by asking new users to rate a small seed set of representative items or by asking representative users to rate a new item. The question is how to build a seed set that can give enough preference information for making good recommendations. One of the most successful approaches, called Representative Based Matrix Factorization, is based on Maxvol algorithm. Unfortunately, this approach has one important limitation — a seed set of a particular size requires a rating matrix factorization of fixed rank that should coincide with that size. This is not necessarily optimal in the general case. In the current paper, we introduce a fast algorithm for an analytical generalization of this approach that we call Rectangular Maxvol. It allows the rank of factorization to be lower than the required size of the seed set. Moreover, the paper includes the theoretical analysis of the method's error, the complexity analysis of the existing methods and the comparison to the state-of-the-art approaches.

Labeled training data is an essential part of any supervised machine learning framework. In practice, there is a trade-off between the quality of a label and its cost. In this paper, we consider a problem of learning to rank on a large-scale dataset with low-quality relevance labels aiming at maximizing the quality of a trained ranker on a small validation dataset with high-quality ground truth relevance labels. Motivated by the classical Gauss-Markov theorem for the linear regression problem, we formulate the problems of (1) reweighting training instances and (2) remapping learning targets. We propose meta–gradient decision tree learning framework for optimizing weight and target functions by applying gradient-based hyperparameter optimization. Experiments on a large-scale real-world dataset demonstrate that we can significantly improve state-of-the-art machine-learning algorithms by incorporating our framework.

Nowadays, the development of most leading web services is controlled by online experiments that qualify and quantify the steady stream of their updates achieving more than a thousand concurrent experiments per day. Despite the increasing need for running more experiments, these services are limited in their user traffic. This situation leads to the problem of finding a new or improving existing key performance metric with a higher sensitivity and lower variance. We focus on the problem of variance reduction for engagement metrics of user loyalty that are widely used in A/B testing of web services. We develop a general framework that is based on evaluation of the mean difference between the actual and the approximated values of the key performance metric (instead of the mean of this metric). On the one hand, it allows us to incorporate the state-of-the-art techniques widely used in randomized experiments of clinical and social research, but limitedly used in online evaluation. On the other hand, we propose a new class of methods based on advanced machine learning algorithms, including ensembles of decision trees, that, to the best of our knowledge, have not been applied earlier to the problem of variance reduction. We validate the variance reduction approaches on a very large set of real large-scale A/B experiments run at Yandex for different engagement metrics of user loyalty. Our best approach demonstrates $63\%$ average variance reduction (which is equivalent to 63% saved user traffic) and detects the treatment effect in $2$ times more A/B experiments.

In this paper, we consider a non-convex loss-minimization problem of learning Supervised PageRank models, which can account for features of nodes and edges. We propose gradient-based and random gradient-free methods to solve this problem. Our algorithms are based on the concept of an inexact oracle and unlike the state-of-the-art gradient-based method we manage to provide theoretically the convergence rate guarantees for both of them. Finally, we compare the performance of the proposed optimization methods with the state of the art applied to a ranking task.

This study introduces a novel feature selection approach CMICOT, which is a further evolution of filter methods with sequential forward selection (SFS) whose scoring functions are based on conditional mutual information (MI). We state and study a novel saddle point (max-min) optimization problem to build a scoring function that is able to identify joint interactions between several features. This method fills the gap of MI-based SFS techniques with high-order dependencies. In this high-dimensional case, the estimation of MI has prohibitively high sample complexity. We mitigate this cost using a greedy approximation and binary representatives what makes our technique able to be effectively used. The superiority of our approach is demonstrated by comparison with recently proposed interaction-aware filters and several interaction-agnostic state-of-the-art ones on ten publicly available benchmark datasets.

Relevance labels is the essential part of any learning to rank framework. The rapid development of crowdsourcing platforms led to a significant reduction of the cost of manual labeling. This makes it possible to collect very large sets of labeled documents to train a ranking algorithm. However, relevance labels acquired via crowdsourcing are typically coarse and noisy, so certain consensus models are used to measure the quality of labels and to reduce the noise. This noise is likely to affect a ranker trained on such labels, and, since none of the existing consensus models directly optimizes ranking quality, one has to apply some heuristics to utilize the output of a consensus model in a ranking algorithm, e.g., to use majority voting among workers to get consensus labels. The major goal of this paper is to unify existing approaches to consensus modeling and noise reduction within a learning to rank framework. Namely, we present a machine learning algorithm aimed at improving the performance of a ranker trained on a crowdsourced dataset by proper remapping of labels and reweighting of samples. In the experimental part, we use several characteristics of workers/labels extracted via various consensus models in order to learn the remapping and reweighting functions. Our experiments on a large-scale dataset demonstrate that we can significantly improve state-of-the-art machine-learning algorithms by incorporating our framework.

Online controlled experiments, e.g., A/B testing, is the state-of-the-art approach used by modern Internet companies to improve their services based on data-driven decisions. The most challenging problem is to define an appropriate online metric of user behavior, so-called Overall Evaluation Criterion (OEC), which is both interpretable and sensitive. A typical OEC consists of a key metric and an evaluation statistic. Sensitivity of an OEC to the treatment effect of an A/B test is measured by a statistical significance test. We introduce the notion of Overall Acceptance Criterion (OAC) that includes both the components of an OEC and a statistical significance test. While existing studies on A/B tests are mostly concentrated on the first component of an OAC, its key metric, we widely study the two latter ones by comparison of several statistics and several statistical tests with respect to user engagement metrics on hundreds of A/B experiments run on real users of Yandex. We discovered that the application of the state-of-the-art Student's t-tests to several main user engagement metrics may lead to an underestimation of the false-positive rate by an order of magnitude. We investigate both well-known and novel techniques to overcome this issue in practical settings. At last, we propose the entropy and the quantiles as novel OECs that reflect the diversity and extreme cases of user engagement.

It is well known that a great number of query--document features which significantly improve the quality of ranking for popular queries, however, do not provide any benefit for new or rare queries since there is typically not enough data associated with those queries that is required to reliably compute the values of those features. It is a common practice to propagate the values of such features from popular to tail queries, if the queries are similar according to some predefined query similarity functions. In this paper, we propose new algorithms that facilitate and increase the effectiveness of this propagation. Given a query similarity function and a query--document relevance feature, we introduce two different approaches (linear weighting approach and tree-based approach) to learn a function of values of the similarity function and values of the feature for the similar queries w.r.t. the given document. The propagated value of the feature equals the value of the obtained function for the given query--document pair.

Nowadays, the development of most leading web services is controlled by online experiments that qualify and quantify the steady stream of their updates. The challenging problem is to define an appropriate online metric of user behavior, so-called Overall Evaluation Criterion (OEC), which is both interpretable and sensitive. The state-of-the-art approach is to choose a type of entities to observe in the behavior data, to define a key metric for these observations, and to estimate the average value of this metric over the observations in each of the system versions. A significant disadvantage of the OEC obtained in this way is that the average value of the key metric does not necessarily change, even if its distribution changes significantly. The reason is that the difference between the mean values of the key metric over the two variants of the system does not necessarily reflect the character of the change in the distribution.

Nowadays, billions of people use the Web in connection with their daily needs. A significant part of the needs are constituted by search tasks that are usually addressed by search engines. Thus, daily search needs result in regular user engagement with a search engine. User engagement with web sites and services was studied in various aspects, but there appear to be no studies of its regularity and periodicity. In this paper, we studied periodicity of the user engagement with a popular search engine through applying spectrum analysis to temporal sequences of different engagement metrics. We found periodicity patterns of user engagement and revealed classes of users whose periodicity patterns do not change over a long period of time. In addition, we used the spectrum series as metrics to evaluate search quality.

Modern Internet companies improve their services by means of data-driven decisions that are based on online controlled experiments (also known as A/B tests). To run more online controlled experiments and to get statistically significant results faster are the emerging needs for these companies. The main way to achieve these goals is to improve the sensitivity of A/B experiments. We propose a novel approach to improve the sensitivity of user engagement metrics (that are widely used in A/B tests) by utilizing prediction of the future behavior of an individual user. This problem of prediction of the exact value of a user engagement metric is also novel and is studied in our work. We demonstrate the effectiveness of our sensitivity improvement approach on several real online experiments run at Yandex. Especially, we show how it can be used to detect the treatment effect of an A/B test faster with the same level of statistical significance.

Implicit feedback from users of a web search engine is an essential source providing consistent personal relevance labels from the actual population of users. However, previous studies on personalized search employ this source in a rather straightforward manner. Basically, documents that were clicked on get maximal gain, and the rest of the documents are assigned the zero gain. As we demonstrate in our paper, a ranking algorithm trained using these gains directly as the ground truth relevance labels leads to a suboptimal personalized ranking.

Given a repeatedly issued query and a document with a not-yet-confirmed potential to satisfy the users' needs, a search system should place this document on a high position in order to gather user feedback and obtain a more confident estimate of the document utility. On the other hand, the main objective of the search system is to maximize expected user satisfaction over a rather long period, what requires showing more relevant documents on average. The state-of-the-art approaches to solving this exploration-exploitation dilemma rely on strongly simplified settings making these approaches infeasible in practice. We improve the most flexible and pragmatic of them to handle some actual practical issues. The first one is utilizing prior information about queries and documents, the second is combining bandit-based learning approaches with a default production ranking algorithm. We show experimentally that our framework enables to significantly improve the ranking of a leading commercial search engine.

Graph-based ranking plays a key role in many applications, such as web search and social computing. Pioneering methods of ranking on graphs (e.g., PageRank and HITS) computed ranking scores relying only on the graph structure. Recently proposed methods, such as Semi-Supervised Page-Rank, take into account both the graph structure and the metadata associated with nodes and edges in a unified optimization framework. Such approaches are based on initializing the underlying random walk models with prior weights of nodes and edges that in turn depend on their individual properties. While in those models the prior weights of nodes and edges depend only on their own features, one can also assume that such weights may also depend or be related to the prior weights of their neighbors. This paper addresses the problem of weighting nodes and edges according to this intuition by realizing it in a general ranking model and an efficient algorithm of tuning the parameters of that model.

The task of discovering places of interest is a key step for many location-based recommendation tasks. In this paper we propose a fully unsupervised and parameter-free approach to deal with this problem based on the collection of geotagged photos. While previous papers are mostly devoted to discovering points (POI), we focus on areas of interest (AOI). Recommendation of better matches the traditional tourist goals and allows to robustly incorporate the interests of many users resulting in less subjective recommendations. The typical question that can be answered with the algorithm is formulated as "Where can one spend T minutes/hours walking around to observe as many attractive places as possible?"

With increasing popularity of browser toolbars, the challenge of employing user behavior data stored in their logs rises in its importance. The analysis of post-click search trails was shown to provide important knowledge about user experience, helpful for improving existing search systems. However, the utility of different trail properties for improving existing ranking models is still underexplored. We conduct a large-scale study and evaluation of a rich set of search trail features in realistic settings and conclude that a deeper investigation of a users experience far beyond her click on the result page has the potential to improve the existing ranking models.

With the ever-increasing speed of content turnover on the web, it is particularly important to understand the patterns that pages' popularity follows. This paper focuses on the dynamical part of the web, i.e. pages that have a limited lifespan and experience a short popularity outburst within it. We classify these pages into five patterns based on how quickly they gain popularity and how quickly they lose it. We study the properties of pages that belong to each pattern and determine content topics that contain disproportionately high fractions of particular patterns. These developments are utilized to create an algorithm that approximates with reasonable accuracy the expected popularity pattern of a web page based on its URL and, if available, prior knowledge about its domain's topics.

In the last years, a lot of attention was attracted by the problem of page authority computation based on user browsing behavior. However, the proposed methods have a number of limitations. In particular, they run on a single snapshot of a user browsing graph ignoring substantially dynamic nature of user browsing activity, which makes such methods recency unaware. This paper proposes a new method for computing page importance, referred to as Fresh BrowseRank. The score of a page by our algorithm equals to the weight in a stationary distribution of a flexible random walk, which is controlled by recency-sensitive weights of vertices and edges. Our method generalizes some previous approaches, provides better capability for capturing the dynamics of the Web and users behavior, and overcomes essential limitations of BrowseRank. The experimental results demonstrate that our method enables to achieve more relevant and fresh ranking results than the classic BrowseRank.

We develop an automatic pipeline that allows ordinary users to capture complete and fully textured 3D models of themselves in minutes, using only a single Kinect sensor, in the uncontrolled lighting environment of their own home. Our method requires neither a turntable nor a second operator, and is robust to the small deformations and changes of pose that inevitably arise during scanning. After the users rotate themselves with the same pose for a few scans from different views, our system stitches together the captured scans using multi-view non-rigid registration, and produces watertight final models. To ensure consistent texturing, we recover the underlying albedo from each scanned texture and generate seamless global textures using Poisson blending. Despite the minimal requirements we place on the hardware and users, our method is suitable for full body capture of challenging scenes that cannot be handled well using previous methods, such as those involving loose clothing, complex poses, and props.

Retweet cascades play an essential role in information diffusion in Twitter. Popular tweets reflect the current trends in Twitter, while Twitter itself is one of the most important online media. Thus, understanding the reasons why a tweet becomes popular is of great interest for sociologists, marketers and social media researches. What is even more important is the possibility to make a prognosis of a tweet's future popularity. Besides the scientific significance of such possibility, this sort of prediction has lots of practical applications such as breaking news detection, viral marketing etc. In this paper we try to forecast how many retweets a given tweet will gain during a fixed time period. We train an algorithm that predicts the number of retweets during time T since the initial moment. In addition to a standard set of features we utilize several new ones. One of the most important features is the flow of the cascade. Another one is PageRank on the retweet graph, which can be considered as the measure of influence of users.

Traditional link-based web ranking algorithms run on a single web snapshot without concern of the dynamics of web pages and links. In particular, the correlation of web pages freshness and their classic PageRank is negative (see [11]). For this reason, in recent years a number of authors introduce some algorithms of PageRank actualization. We introduce our new algorithm called Actual PageRank, which generalizes some previous approaches and therefore provides better capability for capturing the dynamics of the Web. To the best of our knowledge we are the first to conduct ranking evaluations of a fresh-aware variation of PageRank on a large data set. The results demonstrate that our method achieves more relevant and fresh results than both classic PageRank and its "fresh" modifications.

We consider the Buckley-Osthus implementation of preferential attachment and its ability to model the web host graph in two aspects. One is the degree distribution that we observe to follow the power law, as often being the case for real-world graphs. Another one is the two-dimensional edge distribution, the number of edges between vertices of given degrees. We fit a single "initial attractiveness" parameter a of the model, first with respect to the degree distribution of the web host graph, and then, absolutely independently, with respect to the edge distribution. Surprisingly, the values of a we obtain turn out to be nearly the same. Therefore the same model with the same value of the parameter a fits very well the two independent and basic aspects of the web host graph. In addition, we demonstrate that other models completely lack the asymptotic behavior of the edge distribution of the web host graph, even when accurately capturing the degree distribution.