With the increasing number of web documents, the demand for translation has increased dramatically. Non-autoregressive translation (NAT) models can significantly reduce decoding latency to meet the growing translation needs, but they sacrifice translation quality. And there is still an irreparable performance gap between NAT models and strong autoregressive translation (AT) models at the corpus level. However, more fine-grained comparative experiments on AT and NAT are currently lacking. Therefore, in this paper, we first conducted analysis experiments at the sentence level and found complementarity and high similarity between the translations generated by AT and NAT. Then, based on this observation, we propose a general and effective method called NAT4AT, which can not only use NAT to speed up the inference speed of AT significantly but also improve its final translation quality. Specifically, NAT4AT first uses a NAT model to generate an original translation in parallel and then uses an AT model as a correction model to revise errors in the original translation. In this way, the AT model no longer needs to predict the entire translation but only needs to predict a small number of error parts in the NAT result. Extensive experimental results on major WMT benchmarks verify the generality and effectiveness of our method, whose translation quality is superior to the strong AT model and achieves a 5.0x speedup.

Graph partitioning is crucial in distributed graph-parallel computing systems, and it is challenging for graph partitioning to optimize the communication cost and load balancing together. Existing state-of-the-art works, such as Powerlyra and TopoX, optimize the load balancing by randomly distributing the edges of high-degree vertices, which inevitably brings a high communication cost that is unbounded. This paper proposes a graph partition model that can minimize communication cost while maximizing load balancing. More specifically, we model the graph partition as the combinatorial design problem. Our proposed model can provide high-quality partition that guarantees that the computing load can be evenly distributed to each worker and minimizes the communication cost with a near-optimal theoretical boundary.Based on the proposed model, we extend the hybrid-cut partitioning algorithm for the power-law graph and propose HCPD, a hybrid-cut partitioning algorithm based on combinatorial design. HCPD uses the proposed model to optimize the load balancing and communication cost simultaneously for high-degree vertices, and assigns the high-degree vertices and their low-degree neighbors to the same workers by label propagation to reduce the overall communication cost. In this way, we partition the low-degree and high-degree vertices holistically and further improve the partition quality, unlike Powerlyra and TopoX, which deal with the two parts independently. Our experiments show that HCPD outperforms Powerlyra on PageRank task by up to 2× faster on real-world power-law graphs with billions of edges.

Finding frequent subgraph patterns in a big graph is an important problem with many applications such as classifying chemical compounds and building indexes to speed up graph queries. Since this problem is NP-hard, some recent parallel systems have been developed to accelerate the mining. However, they often have a huge memory cost, very long running time, suboptimal load balancing, and possibly inaccurate results. In this paper, we propose an efficient system called T-FSM for parallel mining of frequent subgraph patterns in a big graph. T-FSM adopts a novel task-based execution engine design to ensure high concurrency, bounded memory consumption, and effective load balancing. It also supports a new anti-monotonic frequentness measure called Fraction-Score, which is more accurate than the widely used MNI measure. Our experiments show that T-FSM is orders of magnitude faster than SOTA systems for frequent subgraph pattern mining. Our system code has been released at https://github.com/lyuheng/T-FSM.

Fairness has been taken as a critical metric in machine learning models, which is considered as an important component of trustworthy machine learning. In this paper, we focus on obtaining fairness for popular link prediction tasks, which are measured by dyadic fairness. A novel pre-processing methodology is proposed to establish dyadic fairness through data repairing based on optimal transport theory. With the well-established theoretical connection between the dyadic fairness for graph link prediction and a conditional distribution alignment problem, the dyadic repairing scheme can be equivalently transformed into a conditional distribution alignment problem. Furthermore, an optimal transport-based dyadic fairness algorithm called DyadicOT is obtained by efficiently solving the alignment problem, satisfying flexibility and unambiguity requirements. The proposed DyadicOT algorithm shows superior results in obtaining fairness compared to other fairness methods on two benchmark graph datasets.

Session-based Recommendation (SR) aims to predict the next item for recommendation based on previously recorded sessions of user interaction. The majority of existing approaches to SR focus on modeling the transition patterns of items. In such models, the so-called micro-behaviors describing how the user locates an item and carries out various activities on it (e.g., click, add-to-cart, and read-comments), are simply ignored. A few recent studies have tried to incorporate the sequential patterns of micro-behaviors into SR models. However, those sequential models still cannot effectively capture all the inherent interdependencies between micro-behavior operations. In this work, we aim to investigate the effects of the micro-behavior information in SR systematically. Specifically, we identify two different patterns of micro-behaviors: “sequential patterns” and “dyadic relational patterns”. To build a unified model of user micro-behaviors, we first devise a multigraph to aggregate the sequential patterns from different items via a graph neural network, and then utilize an extended self-attention network to exploit the pair-wise relational patterns of micro-behaviors. Extensive experiments on three public real-world datasets show the superiority of the proposed approach over the state-of-the-art baselines and confirm the usefulness of these two different micro-behavior patterns for SR.

In online shopping, ever-changing fashion trends make merchants need to prepare more differentiated products to meet the diversified demands, and e-commerce platforms need to capture the market trend with a prophetic vision. For the trend prediction, the attribute tags, as the essential description of items, can genuinely reflect the decision basis of consumers. However, few existing works explore the attribute trend in the specific community for e-commerce. In this paper, we focus on the community trend prediction on the item attribute and propose a unified framework that combines the dynamic evolution of two graph patterns to predict the attribute trend in a specific community. Specifically, we first design a community-attribute bipartite graph at each time step to learn the collaboration of different communities. Next, we transform the bipartite graph into a hypergraph to exploit the associations of different attribute tags in one community. Lastly, we introduce a dynamic evolution component based on the recurrent neural networks to capture the fashion trend of attribute tags. Extensive experiments on three real-world datasets in a large e-commerce platform show the superiority of the proposed approach over several strong alternatives and demonstrate the ability to discover the community trend in advance.

Given a data matrix \( D \) , a submatrix \( S \) of \( D \) is an order-preserving submatrix (OPSM) if there is a permutation of the columns of \( S \) , under which the entry values of each row in \( S \) are strictly increasing. OPSM mining is widely used in real-life applications such as identifying coexpressed genes and finding customers with similar preference. However, noise is ubiquitous in real data matrices due to variable experimental conditions and measurement errors, which makes conventional OPSM mining algorithms inapplicable. No previous work on OPSM has ever considered uncertain value intervals using the well-established possible world semantics. We establish two different definitions of significant OPSMs based on thepossible world semantics: (1) expected support-based and (2) probabilistic frequentness-based. An optimized dynamic programming approach is proposed to compute the probability that a row supports a particular column permutation, with a closed-form formula derived to efficiently handle the special case of uniform value distribution and an accurate cubic spline approximation approach that works well with any uncertain value distributions. To efficiently check the probabilistic frequentness, several effective pruning rules are designed to efficiently prune insignificant OPSMs; two approximation techniques based on the Poisson and Gaussian distributions, respectively, are proposed for further speedup. These techniques are integrated into our two OPSM mining algorithms, based on prefix-projection and Apriori, respectively. We further parallelize our prefix-projection-based mining algorithm using PrefixFPM, a recently proposed framework for parallel frequent pattern mining, and we achieve a good speedup with the number of CPU cores. Extensive experiments on real microarray data demonstrate that the OPSMs found by our algorithms have a much higher quality than those found by existing approaches.

In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, the concerns about how to standardize open source implementation of recommendation algorithms continually increase in the research community. In the light of this challenge, we propose a unified, comprehensive and efficient recommender system library called RecBole (pronounced as [rEk'[email protected]]), which provides a unified framework to develop and reproduce recommendation algorithms for research purpose. In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation. We implement the RecBole library based on PyTorch, which is one of the most popular deep learning frameworks. Our library is featured in many aspects, including general and extensible data structures, comprehensive benchmark models and datasets, efficient GPU-accelerated execution, and extensive and standard evaluation protocols. We provide a series of auxiliary functions, tools, and scripts to facilitate the use of this library, such as automatic parameter tuning and break-point resume. Such a framework is useful to standardize the implementation and evaluation of recommender systems. The project and documents are released at https://recbole.io/.

With the development of e-commerce platforms, e-commerce recommendation systems are playing an increasingly important role for the purpose of product recommendation. As a new attack model against e-commerce recommendation systems, the "Ride Item's Coattails" attack creates fake click information to establish the deceptive correlation between popular products and low-quality products in order to mislead the recommendation system of e-commerce platform to boost the sales of low-quality products. This attack is characterized by high concealment and strong destructiveness, which can cause great damage to e-commerce recommendation systems, and adversely affect the usability of the e-commerce platform and users' shopping experience. It is therefore of great practical significance to study how to quickly and effectively identify the false click information and the corresponding "Ride Item's Coattails" attack to better safeguard e-commerce recommendation systems. At present, there is no previously reported relevant research work conducted specifically for addressing the detection of the "Ride Item's Coattails" attack. In this work, we carried out pioneering work in analyzing and summarizing the characteristics of the false click information produced by attackers on the target products in the "Ride Item's Coattails" attack and designed a set of attack detection techniques suitable for e-commerce recommendation systems. Experimental results on real e-commerce datasets show that our proposed techniques can quickly and effectively detect the large-scale fake click information as well as the associated "Ride Item's Coattails" attack in e-commerce recommendation systems.

Frequent pattern mining (FPM) has been a focused theme in data mining research for decades, but there lacks a general programming framework that can be easily customized to mine different kinds of frequent patterns, and existing solutions to FPM over big transaction databases are IO-bound rendering CPU cores underutilized even though FPM is NP-hard. This paper presents, PrefixFPM, a general-purpose framework for FPM that is able to fully utilize the CPU cores in a multicore machine. PrefixFPM follows the idea of prefix projection to partition the workloads of PFM into independent tasks by divide and conquer. PrefixFPM exposes a unified programming interface to users who can customize it to mine their desired patterns, and the parallel execution engine is transparent to end-users and can be reused for mining all kinds of patterns. We have adapted the state-of-the-art serial algorithms for mining frequent patterns including subsequences, subtrees, and subgraphs on top of PrefixFPM, and extensive experiments demonstrate an excellent speedup ratio of PrefixFPM with the number of cores. A demo is available at https://youtu.be/PfioC0GDpsw; the code is available at https://github.com/yanlab19870714/PrefixFPM.

Herb recommendation plays a crucial role in the therapeutic process of Traditional Chinese Medicine (TCM), which aims to recommend a set of herbs to treat the symptoms of a patient. While several machine learning methods have been developed for herb recommendation, they are limited in modeling only the interactions between herbs and symptoms, and ignoring the intermediate process of syndrome induction. When performing TCM diagnostics, an experienced doctor typically induces syndromes from the patient's symptoms and then suggests herbs based on the induced syndromes. As such, we believe the induction of syndromes - an overall description of the symptoms - is important for herb recommendation and should be properly handled. However, due to the ambiguity and complexity of syndrome induction, most prescriptions lack the explicit ground truth of syndromes. In this paper, we propose a new method that takes the implicit syndrome induction process into account for herb recommendation. Specifically, given a set of symptoms to treat, we aim to generate an overall syndrome representation by effectively fusing the embeddings of all the symptoms in the set, so as to mimic how a doctor induces the syndromes. Towards symptom embedding learning, we additionally construct a symptom-symptom graph from the input prescriptions for capturing the relations (cooccurred patterns) between symptoms; we then build graph convolution networks (GCNs) on both symptom-symptom and symptom-herb graphs to learn symptom embedding. Similarly, we construct a herb-herb graph and build GCNs on both herbherb and symptom-herb graphs to learn herb embedding, which is finally interacted with the syndrome representation to predict the scores of herbs. The advantage of such a Multi-Graph GCN architecture is that more comprehensive representations can be obtained for symptoms and herbs. We conduct extensive experiments on a public TCM dataset, demonstrating significant improvements over state-of-the-art herb recommendation methods. Further studies justify the effectiveness of our design of syndrome representation and multiple graphs.

Window functions have been a part of the SQL standard since 2003 and have been studied extensively during the past decade. They are widely used in data analysis; almost all the current mainstream commercial databases support window functions. However, in recent years the size of datasets is growing steeply; the existing window function implementations are not efficient enough. Recently, some sampling-based algorithms (e.g., online aggregation) are proposed to deal with large and complex data in relational databases, which offer us a flexible trade-off between accuracy and efficiency. However, few sampling techniques has been considered for window functions in databases. In this paper, we extend our previous work (Song et al. in Asia-Pacific web and web-age information management joint conference on web and big data, Springer, pp 229–244, 2017 ) and proposed two new algorithms: range-based global sampling algorithm and row-labeled sampling algorithm. The proposed algorithms use global sampling rather than local sampling and are more efficient than other existing algorithms. And we find our proposed algorithms out performed the baseline method over the TPC-H benchmark dataset.

It is an important problem in computational advertising to study the effects of different advertising channels upon user conversions, as advertisers can use the discoveries to plan or optimize advertising campaigns. In this paper, we propose a novel Probabilistic Multi-Touch Attribution (PMTA) model which takes into account not only which ads have been viewed or clicked by the user but also when each such interaction occurred. Borrowing the techniques from survival analysis, we use the Weibull distribution to describe the observed conversion delay and use the hazard rate of conversion to measure the influence of an ad exposure. It has been shown by extensive experiments on a large real-world dataset that our proposed model is superior to state-of-the-art methods in both conversion prediction and attribution analysis. Furthermore, a surprising research finding obtained from this dataset is that search ads are often not the root cause of final conversions but just the consequence of previously viewed ads.

Outsourcing keys (including passwords and data encryption keys) to professional password managers (honest-butcurious service providers) is attracting more and more attention from the researchers and users in the era of cloud computing. However, existing solutions in traditional data outsourcing scenario are unable to simultaneously meet the following three security requirements for keys outsourcing: (1) Confidentiality and privacy of keys; (2) Search privacy on identity attributes tied to keys; (3) Owner controllable authorization over his/her shared keys. In this paper, we propose CloudKeyBank, the first unified key management framework that addresses all the three goals above. To implement CloudKeyBank efficiently, we propose a new cryptographic primitive named Searchable Conditional Proxy Re-Encryption (SC-PRE) which combines the techniques of Hidden Vector Encryption (HVE) and Proxy Re-Encryption (PRE) seamlessly.

How can we know whether one classifier is really better than the other? In the area of text classification, since the publication of Yang and Liu's seminal SIGIR-1999 paper, it has become a standard practice for researchers to apply null-hypothesis significance testing (NHST) on their experimental results in order to establish the superiority of a classifier. However, such a frequentist approach has a number of inherent deficiencies and limitations, e.g., the inability to accept the null hypothesis (that the two classifiers perform equally well), the difficulty to compare commonly-used multivariate performance measures like F1 scores instead of accuracy, and so on. In this paper, we propose a novel Bayesian approach to the performance comparison of text classifiers, and argue its advantages over the traditional frequentist approach based on t-test etc. In contrast to the existing probabilistic model for F1 scores which is unpaired, our proposed model takes the correlation between classifiers into account and thus achieves greater statistical power. Using several typical text classification algorithms and a benchmark dataset, we demonstrate that the our approach provides rich information about the difference between two classifiers' performances.

Item recommendation helps people to discover their potentially interested items among large numbers of items. One most common application is to recommend top-n items on implicit feedback datasets (e.g., listening history, watching history or visiting history). In this paper, we assume that the implicit feedback matrix has local property, where the original matrix is not globally low rank but some sub-matrices are low rank. In this paper, we propose Local Weighted Matrix Factorization (LWMF) for top-n recommendation by employing the kernel function to intensify local property and the weight function to model user preferences. The problem of sparsity can also be relieved by sub-matrix factorization in LWMF, since the density of sub-matrices is much higher than the original matrix. We propose a heuristic method to select sub-matrices which approximate the original matrix well. The greedy algorithm has approximation guarantee of factor $$1-\frac{1}{e}$$ to get a near-optimal solution. The experimental results on two real datasets show that the recommendation precision and recall of LWMF are both improved about 30% comparing with the best case of weighted matrix factorization (WMF).

In modern cities, more and more people drive the vehicles, equipped with the GPS devices, which create a large scale of trajectories. Gathering and analyzing these large-scale trajectory data provide a new opportunity to understand the city dynamics and to reveal the hidden social and economic phenomena. This paper designs and implements a tool, named as PGWinFunc, to analyze trajectory data by extending a traditional relational database. Firstly we introduce some efficient query process and optimization methods for SQL Window Aggregate Functions in PostgreSQL. Secondly, we present how to mine the LBS (Location-Based Service) patterns, such as the average speed and traffic flow, from the large-scale trajectories with SQL expression with Window Aggregate Functions. Finally, the effectiveness and efficiency of the PGWinFunc tool are demonstrated and we also visualized the results by BAIDU MAP.

In multi-class text classification, the performance (effectiveness) of a classifier is usually measured by micro-averaged and macro-averaged F1 scores. However, the scores themselves do not tell us how reliable they are in terms of forecasting the classifier's future performance on unseen data. In this paper, we propose a novel approach to explicitly modelling the uncertainty of average F1 scores through Bayesian reasoning, and demonstrate that it can provide much more comprehensive performance comparison between text classifiers than the traditional frequentist null hypothesis significance testing (NHST).

Numerous applications such as data integration, protein detection, and article copy detection share a similar core problem: given a string as the query, how to efficiently find all the similar answers from a large scale string collection. Many existing methods adopt a prefix-filter-based framework to solve this problem, and a number of recent works aim to use advanced filters to improve the overall search performance. In this paper, we propose a gram-based framework to achieve near maximum filter performance. The main idea is to judiciously choose the high-quality grams as the prefix of query according to their estimated ability to filter candidates. As this selection process is proved to be NP-hard problem, we give a cost model to measure the filter ability of grams and develop efficient heuristic algorithms to find high-quality grams. Extensive experiments on real datasets demonstrate the superiority of the proposed framework in comparison with the state-of-art approaches.

Explosive growth in the number of passwords for Web based applications and encryption keys for outsourced data storage well exceeds the management limit of users. Therefore, outsourcing keys (including passwords and data encryption keys) to professional password managers (honest-but-curious service providers) is attracting the attention of many users. However, existing solutions in a traditional data outsourcing scenario are unable to simultaneously meet the following three security requirements for keys outsourcing: (1) Confidentiality and privacy of keys; (2) Search privacy on identity attributes tied to keys; (3) Owner controllable authorization over his/her shared keys. In this paper, we propose CloudKeyBank, the first unified key management framework that addresses all the three goals above. Under our framework, the key owner can perform privacy and controllable authorization enforced encryption with minimum information leakage. To implement CloudKeyBank efficiently, we propose a new cryptographic primitive named Searchable Conditional Proxy Re-Encryption (SC-PRE) which combines the techniques of Hidden Vector Encryption (HVE) and Proxy Re-Encryption (PRE) seamlessly, and propose a concrete SC-PRE scheme based on existing HVE and PRE schemes. Our experimental results and security analysis show the efficiency and security goals are well achieved.

Detecting review spam is important for current e-commerce applications. However, the posted order of review has been neglected by the former work. In this paper, we explore the issue on fake review detection in review sequence, which is crucial for implementing online anti-opinion spam. We analyze the characteristics of fake reviews firstly. Based on review contents and reviewer behaviors, six time sensitive features are proposed to highlight the fake reviews. And then, we devise supervised solutions and a threshold-based solution to spot the fake reviews as early as possible. The experimental results show that our methods can identify the fake reviews orderly with high precision and recall.

Tracking data provenance (or lineage) has become increasingly important in many large-scale applications, and a few methods have been proposed to record data provenance recently. However, most of previous works mainly focus on deterministic databases except Trio style lineage that aims at probabilistic databases, which is much more challenging because of the exponential growth of possible world instances and dependence among intermediate tuples. This paper proposes an approach, named PHP-tree, to model how-provenance upon probabilistic databases. we also show how to evaluate probability based on a PHP-tree. Compared with Trio style lineage, our approach is independent of intermediate results and can calculate the probability both cases of restricted and complete propagation of data provenance. Detailed experimental results show the effectiveness, efficiency and scalability of our proposed model.

Cloud provides dynamically computing services for large scales of data over the Internet. IaaS(information as a service) is one of utilities to provide information service in Cloud computing. Large scales of XML data are produced continually in Internet. Efficient information filtering services are needed. Previous XML filter approaches aim at XPath queries. However, many users tend to use keywords to describe requirements. SLCA (Smallest Lowest Common Ancestor)-based XML keyword search is one of the most important information retrieval approaches. Former approaches focus on building centralized index for a large scale of XML document collection and can't process continuous XML streams. This paper addresses SLCA computing service for continuous XML document. A novel SLCA computing service is designed, where SLCAs are obtained in one scan of XML stream. We demonstrate the efficiency of our algorithms analytically and experimentally.

No abstract available.

Given a document repository, search engine is very helpful to retrieve information. Currently, vertical search is a hot topic, and Google Scholar [4] is an example for academic search. However, most vertical search engines only return the flat ranked list without an efficient result exhibition for given users. We study this problem and designed a vertical search engine prototype Dolphin, where the flexible user-oriented templates can be defined and the survey-like results are presented according to the template.