Visualization recommendation aims to enable rapid visual analysis of massive datasets. In real-world scenarios, it is essential to quickly gather and comprehend user preferences to cover users from diverse backgrounds, including varying skill levels and analytical tasks. Previous approaches to personalized visualization recommendations are non-interactive and rely on initial user data for new users. As a result, these models cannot effectively explore options or adapt to real-time feedback. To address this limitation, we propose an interactive personalized visualization recommendation (PVisRec) system that learns on user feedback from previous interactions. For more interactive and accurate recommendations, we propose Hier-SUCB, a contextual combinatorial semi-bandit in the PVisRec setting. Theoretically, we show an improved overall regret bound with the same rank of time but an improved rank of action space. We further demonstrate the effectiveness of Hier-SUCB through extensive experiments where it is comparable to offline methods and outperforms other bandit algorithms in the setting of visualization recommendation.

Time-series forecasting models are invariably used in a variety of domains for crucial decision-making. Traditionally these models are constructed by experts with considerable manual effort. Unfortunately, this approach has poor scalability while generating accurate forecasts for new datasets belonging to diverse applications. Without access to skilled domain-knowledge, one approach is to train all the models on the new time-series data and then select the best one. However, this approach is nonviable in practice. In this work, we develop techniques for fast automatic selection of the best forecasting model for a new unseen time-series dataset, without having to first train (or evaluate) all the models on the new time-series data to select the best one. In particular, we develop a forecasting meta-learning approach called AutoForecast that allows for the quick inference of the best time-series forecasting model for an unseen dataset. Our approach learns both forecasting models’ performances over time horizon of the same dataset and task similarity across different datasets. The experiments demonstrate the effectiveness of the approach over state-of-the-art (SOTA) single and ensemble methods and several SOTA meta-learners (adapted to our problem) in terms of selecting better forecasting models (i.e., 2 \(\times\) gain) for unseen tasks for univariate and multivariate testbeds. AutoForecast has also significant reduction in inference time compared to the naïve approach (doing inference using all possible models and then selecting the best one), with median of 42 \(\times\) across the two testbeds. We release our meta-learning database corpus (348 datasets), performances of the 322 forecasting models on the database corpus, meta-features, and source codes for the community to access them for forecasting model selection and to build on them with new datasets and models which can help advance automating time-series forecasting problem. In our released database corpus, we unveil new traces of Adobe computing cluster usage for production workloads.

With the advent of cloud platforms that offer GPU-as-a-Service (GPUaaS), such as Amazon EC2 and Microsoft Azure, researchers increasingly rely on virtual GPU instances for training deep learning (DL) workloads. These GPU instances vary in configuration attributes, including but not limited to the number of GPUs, the number of vCPUs, and per-hour usage cost. Identifying the appropriate GPU instance for training a DL workload becomes extremely difficult due to the huge GPU instance selection space offered by the cloud platforms and the corresponding variation in training performance or computational needs of different DL workloads. In this paper, we propose a GPU instance recommendation system called GPURank, which provides a recommended list of GPU instances to choose from for DL workloads. GPURank predicts and leverages two metrics: epoch training cost and average GPU utilization to make this choice. We curated a new benchmark dataset by profiling diverse DL workloads to train the regression models in GPURank’s prediction framework. We demonstrate that GPURank beats baselines on two pertinent problem settings: (1) unseen workloads and (2) unseen GPU instances, with a 25.89% and 20.10% higher average ranking performance on these respectively.

Modern software systems’ increasing complexity and scale makes it challenging to accurately detect system issues and outages, which have been tackled as an anomaly detection task. Conventionally, such anomalous events barely happen, and annotating them is time-consuming and impractical in big data streams. Even with automated anomaly detection, resolving issues promptly is a remaining challenge that can only be done by providing specific contexts such as root causes, target/affected services, and more. To address these fundamentally important problems, we present Grid Transformer (GT), a framework designed to detect and explain $\log$ anomalies in an unsupervised setting. We first train an Auto-Encoder model to generate pseudo labels. Then, we train the proposed grid transformer that not only predicts anomalies but also generates why a particular instance is an anomaly. Through extensive experiments, we demonstrate the effectiveness of our approach where it is shown to outperform the other $\log$ anomaly detection models by 20% while also able to generate time-wise and message-wise explanations of the anomalies.

Visualization recommendation systems make understanding data more accessible to users of all skill levels by automatically generating visualizations for users to explore. However, most existing visualization recommendation systems focus on ranking all possible visualizations based on the attributes or encodings, which makes it difficult to find the most interesting or relevant insights. We therefore introduce a novel class of visualization recommendation systems that automatically rank and recommend both groups of related insights and the most important insights within each group. Our approach combines results across different learning-based methods to discover insights automatically and generalizes to a variety of attribute types (e.g., categorical, numerical, and temporal), including non-trivial combinations of these attribute types. We then implemented a new insight-centric visualization recommendation system, SpotLight, which ranks annotated visualizations in visual insight groups. Finally, we conducted a user study which showed that users are able to quickly understand and find relevant insights in unfamiliar data.

Short text clustering is a challenging unsupervised learning task which requires a complex representation of each document to effectively model the semantics and syntactic structure of the text. Existing works have attempted to tackle this challenging task by incorporating additional information to the model, such as number of clusters, number of datapoints in each clusters, the distribution of the input data, and more. Unlike previous approaches, we propose to exploit an auxiliary dataset that is fully labeled to augment the quality of the learned representations. We also define the problem as cross domain clustering (XDC), which leverages adversarial learning to train an adaptive clustering model across text domains. Specifically, XDC jointly exploits a labeled source domain and an unlabeled target domain during model training. Owing to domain adversarial learning, the distribution shift across source and target domains could be mitigated. Moreover, XDC is implemented as a linkage-based clustering approach using graphs, which is agnostic of the number of clusters. We evaluate our XDC framework on three text datasets, and results show that it outperforms the state-of-the-art text clustering methods in most cases. Ablation studies and qualitative analysis also demonstrate the effectiveness of our framework.

In this work, we develop techniques for fast automatic selection of the best forecasting model for a new unseen time-series dataset, without having to first train (or evaluate) all the models on the new time-series data to select the best one. In particular, we develop a forecasting meta-learning approach called AutoForecast that allows for the quick inference of the best time-series forecasting model for an unseen dataset. Our approach learns both forecasting models performances over time horizon of same dataset and task similarity across different datasets. The experiments demonstrate the effectiveness of the approach over state-of-the-art (SOTA) single and ensemble methods and several SOTA meta-learners (adapted to our problem) in terms of selecting better forecasting models (i.e., 2X gain) for unseen tasks for univariate and multivariate testbeds.

Sequential recommenders aim to capture users' dynamic interests from their historical action sequences, but remain challenging due to data sparsity issues, as well as the noisy and complex relationships among items in a sequence. Several approaches have sought to alleviate these issues using side-information, such as item content (e.g., images), action types (e.g., click, purchase). While useful, we argue one of the main contextual signals is largely ignored-namely users' queries. When users browse and consume products (e.g., music, movies), their sequential interactions are usually a combination of queries, clicks (etc.). Most interaction datasets discard queries, and corresponding methods simply model sequential behaviors over items and thus ignore this critical context of user interactions.

Web applications utilize Recommendation Systems (RS) to address the problem of consumer over-choices. Recent works have taken advantage of multi-modality or multi-view, input information (such as user interaction, images, texts, rating scores) to boost recommendation system performance compared with using single-modality information. However, the use of multi-modality input demands much higher computational cost and storage capacity. On the other hand, the real-world RS services usually have strict budgets on both time and space for a good customer experience. As a result, the model efficiency of multi-modality recommendation systems has gained increasing importance. While unfortunately, to the best of our knowledge, there is no existing study of a generic compression framework for multi-modality RS. In this paper, we investigate, for the first time, how to compress a multi-modality recommendation system with a fixed budget. Assuming that input information from different modalities are of unequal importance, a good compression algorithm should learn to automatically allocate different resource budgets to each input, based on their importance in maximally preserving recommendation efficacy. To this end, we leverage the tools of neural architecture search (NAS) and distillation and propose Auto Multi-modAlity Recommendation System (AutoMARS), a unified modality-aware model compression framework dedicated to multi-modality recommendation systems. We demonstrate the effectiveness and generality of AutoMARS by testing it on three different Amazon datasets of various sparsity. AutoMARS demonstrates superior multi-modality compression performance than previous state-of-the-art compression methods. For example on the Amazon Beauty dataset, we achieve on average a 20% higher accuracy over previous state-of-the-art methods, while enjoying 65% reduction over baselines. Codes are available at: https://github.com/VITA-Group/AutoMARS.

The recent advances of conversational recommendations provide a promising way to efficiently elicit users' preferences via conversational interactions. To achieve this, the recommender system conducts conversations with users, asking their preferences for different items or item categories. Most existing conversational recommender systems for cold-start users utilize a multi-armed bandit framework to learn users' preference in an online manner. However, they rely on a pre-defined conversation frequency for asking about item categories instead of individual items, which may incur excessive conversational interactions that hurt user experience. To enable more flexible questioning about key-terms, we formulate a new conversational bandit problem that allows the recommender system to choose either a key-term or an item to recommend at each round and explicitly models the rewards of these actions. This motivates us to handle a new exploration-exploitation (EE) trade-off between key-term asking and item recommendation, which requires us to accurately model the relationship between key-term and item rewards. We conduct a survey and analyze a real-world dataset to find that, unlike assumptions made in prior works, key-term rewards are mainly affected by rewards of representative items. We propose two bandit algorithms, Hier-UCB and Hier-LinUCB, that leverage this observed relationship and the hierarchical structure between key-terms and items to efficiently learn which items to recommend. We theoretically prove that our algorithm can reduce the regret bound's dependency on the total number of items from previous work. We validate our proposed algorithms and regret bound on both synthetic and real-world data.

Tabular pre-training models have received increasing attention due to the wide-ranging applications for tabular data analysis. However, most of the existing solutions are directly built upon the tabular data with a mixture of non-semantic and semantic contents. According to the statistics, only 30% of tabular data in wikitables are semantic entities that are surrounded and isolated by enormous irregular characters such as numbers, strings, symbols, etc. Despite the small portion, such semantic entities are crucial for table understanding. This paper attempts to enhance the existing tabular pre-training model by injecting common-sense knowledge from external sources. Compared with the knowledge injection in the natural language pre-training models, the tabular model naturally requires overcoming the domain gaps between external knowledge and tabular data with significant differences in both structures and contents. To this end, we propose the dual-adapters inserted within the pre-trained tabular model for flexible and efficient knowledge injection. The two parallel adapters are trained by the knowledge graph triplets and semantically augmented tables respectively for infusion and alignment with the tabular data. In addition, a path-wise attention layer is attached below to fuse the cross-domain representation with the weighted contribution. Finally, to verify the effectiveness of our proposed knowledge injection framework, we extensively test it on 5 different application scenarios covering both zero-shot and finetuning-based tabular understanding tasks over the cell, column, and tables levels.

Bundle recommender systems recommend sets of items (e.g., pants, shirt, and shoes) to users, but they often suffer from two issues: significant interaction sparsity and a large output space. In this work, we extend multi-round conversational recommendation (MCR) to alleviate these issues. MCR—which uses a conversational paradigm to elicit user interests by asking user preferences on tags (e.g., categories or attributes) and handling user feedback across multiple rounds—is an emerging recommendation setting to acquire user feedback and narrow down the output space, but has not been explored in the context of bundle recommendation.

Interactive recommender systems (IRS) have received wide attention in recent years. To capture users' dynamic preferences and maximize their long-term engagement, IRS are usually formulated as reinforcement learning (RL) problems. Despite the promise to solve complex decision-making problems, RL-based methods generally require a large amount of online interaction, restricting their applications due to economic considerations. One possible direction to alleviate this issue is cross-domain recommendation that aims to leverage abundant logged interaction data from a source domain (e.g., adventure genre in movie recommendation) to improve the recommendation quality in the target domain (e.g., crime genre). Nevertheless, prior studies mostly focus on adapting the static representations of users/items. Few have explored how the temporally dynamic user-item interaction patterns transform across domains.

Multilingual natural language understanding, which aims to comprehend multilingual documents, is an important task. Existing efforts have been focusing on the analysis of centrally stored text data, but in real practice, multilingual data is usually distributed. Federated learning is a promising paradigm to solve this problem, which trains local models with decentralized data on local clients and aggregates local models on the central server to achieve a good global model. However, existing federated learning methods assume that data are independent and identically distributed (IID), and cannot handle multilingual data, that are usually non-IID with severely skewed distributions: First, multilingual data is stored on local client devices such that there are only monolingual or bilingual data stored on each client. This makes it difficult for local models to know the information of documents in other languages. Second, the distribution over different languages could be skewed. High resource language data is much more abundant than low resource language data. The model trained on such skewed data may focus more on high resource languages but fail to consider the key information of low resource languages. To solve the aforementioned challenges of multilingual federated NLU, we propose a plug-and-play knowledge composition (KC) module, called FedKC, which exchanges knowledge among clients without sharing raw data. Specifically, we propose an effective way to calculate a consistency loss defined based on the shared knowledge across clients, which enables models trained on different clients achieve similar predictions on similar data. Leveraging this consistency loss, joint training is thus conducted on distributed data respecting the privacy constraints. We also analyze the potential risk of FedKC and provide theoretical bound to show that it is difficult to recover data from the corrupted data. We conduct extensive experiments on three public multilingual datasets for three typical NLU tasks, including paraphrase identification, question answering matching, and news classification. The experiment results show that the proposed FedKC can outperform state-of-the-art baselines on the three datasets significantly.

Recently, self-attentive models have shown promise in sequential recommendation, given their potential to capture user long-term preferences and short-term dynamics simultaneously. Despite their success, we argue that self-attention modules, as a non-local operator, often fail to capture short-term user dynamics accurately due to a lack of inductive local bias. To examine our hypothesis, we conduct an analytical experiment on controlled 'short-term' scenarios. We observe a significant performance gap between self-attentive recommenders with and without local constraints, which implies that short-term user dynamics are not sufficiently learned by existing self-attentive recommenders. Motivated by this observation, we propose a simple framework, (Locker) for self-attentive recommenders in a plug-and-play fashion. By combining the proposed local encoders with existing global attention heads, Locker enhances short-term user dynamics modeling, while retaining the long-term semantics captured by standard self-attentive encoders. We investigate Locker with five different local methods, outperforming state-of-the-art self-attentive recom- menders on three datasets by 17.19% ([email protected]) on average.

Column annotation, the process of annotating tabular columns with labels, plays a fundamental role in digital marketing data governance. It has a direct impact on how customers manage their data and facilitates compliance with regulations, restrictions, and policies applicable to data use. Despite substantial gains in accuracy brought by recent deep learning-driven column annotation methods, their incapability of explaining why columns are matched with particular target labels has drawn concern, due to the black-box nature of deep neural networks. Such explainability is of particular importance in industrial marketing scenarios, where data stewards need to quickly verify and calibrate the annotation results to ascertain the correctness of downstream applications. This work sheds new light on the explainable column annotation problem, the first of its kind column annotation task. To achieve this, we propose a new approach called EXACTA, which conducts multi-hop knowledge graph reasoning using inverse reinforcement learning to find a path from a column to a potential target label while ensuring both annotation performance and explainability. We experiment on four benchmarks, both publicly available and real-world ones, and undertake a comprehensive analysis on the explainability. The results suggest that our method not only provides competitive annotation performance compared with existing deep learning-based models, but more importantly, produces faithfully explainable paths for annotated columns to facilitate human examination.

Recent research explores incorporating knowledge graphs (KG) into e-commerce recommender systems, not only to achieve better recommendation performance, but more importantly to generate explanations of why particular decisions are made. This can be achieved by explicit KG reasoning, where a model starts from a user node, sequentially determines the next step, and walks towards an item node of potential interest to the user. However, this is challenging due to the huge search space, unknown destination, and sparse signals over the KG, so informative and effective guidance is needed to achieve a satisfactory recommendation quality. To this end, we propose a CoArse-to-FinE neural symbolic reasoning approach (CAFE). It first generates user profiles as coarse sketches of user behaviors, which subsequently guide a path-finding process to derive reasoning paths for recommendations as fine-grained predictions. User profiles can capture prominent user behaviors from the history, and provide valuable signals about which kinds of path patterns are more likely to lead to potential items of interest for the user. To better exploit the user profiles, an improved path-finding algorithm called Profile-guided Path Reasoning (PPR) is also developed, which leverages an inventory of neural symbolic reasoning modules to effectively and efficiently find a batch of paths over a large-scale KG. We extensively experiment on four real-world benchmarks and observe substantial gains in the recommendation performance compared with state-of-the-art methods.

Personalization is essential for enhancing the customer experience in retrieval tasks. In this paper, we develop a novel method CA-GCN for personalized image retrieval in the Adobe Stock image system. The proposed method CA-GCN leverages user behavior data in a Graph Convolutional Neural Network (GCN) model to learn user and image embeddings simultaneously. Standard GCN performs poorly on sparse user-image interaction graphs due to the limited knowledge gain from less representative neighbors. To address this challenge, we propose to augment the sparse user-image interaction data by considering the similarities among images. Specifically, we detect clusters of similar images and introduce a set of hidden super-nodes in the graph to represent clusters. We show that such an augmented graph structure can significantly improve the retrieval performance on real-world data collected from Adobe Stock service. In particular, when testing the proposed method on real users' stock image retrieval sessions, we get promoted average click position from 70 to 51.

Predictive modeling of patient state to state medical conditions in ICU is a critical yet challenging task in health informatics and machine learning. Prior critical stages from the same ICU admission may contribute differently to the next stages. That said, stages are interdependent, and disease progression is a multi-step temporal observation. In this paper, we formally name this problem as “Clinical State Progression Prediction (CSPP).” Conventional temporal modeling may fit well to predictions of fixed size observations and number of stages, but have troubles and less flexibility when addressing CSPP. To that end, we cast this problem as multi-label learning on time series data in which each stage is marked by a label. The implementation of entire framework includes two phases. In learning, an RNN based Encoder-Decoder deep model is developed for basic temporal modeling. In addition, Attention mechanism and Connectionist Temporal Classification (CTC) are integrated to explicitly model the temporal dependency as well as monotonic relation between input time series and output label space. In inference, based on the observed multi-stage labels, a non-parametric retrieval is carried out first to build up the reference patient records. Then, based on CTC-Attention learning model, consistent progressions are computed and ranked to contribute to the prediction of the clinical state progression in the next few hours. Extensive experiments on MIMIC III and Parkinson datasets demonstrate that the proposed predictive modeling for CSPP outperforms state-of-the-art works on Sepsis, Kidney-Sepsis-Mortality, Heart-Sepsis-Mortality, and Parkinson Progression.

The last decade has witnessed a surge of interest in applying deep learning models for discovering sequential patterns from a large volume of data. Recent works show that deep learning models can be further improved by enforcing models to learn a smooth output distribution around each data point. This can be achieved by augmenting training data with slight perturbations that are designed to alter model outputs. Such adversarial training approaches have shown much success in improving the generalization performance of deep learning models on static data, e.g., transaction data or image data captured on a single snapshot. However, when applied to sequential data, the standard adversarial training approaches cannot fully capture the discriminative structure of a sequence. This is because real-world sequential data are often collected over a long period of time and may include much irrelevant information to the classification task. To this end, we develop a novel adversarial training approach for sequential data classification by investigating when and how to perturb a sequence for an effective data augmentation. Finally, we demonstrate the superiority of the proposed method over baselines in a diversity of real-world sequential datasets.

Modeling user behavior from unstructured software log-trace data is critical in providing personalized service (\emphe.g., cross-platform recommendation). Existing user modeling approaches cannot well handle the long-term temporal information in log data, or produce semantically meaningful results for interpreting user logs. To address these challenges, we propose a Log2Intent framework for interpretable user modeling in this paper. Log2Intent adopts a deep sequential modeling framework that contains a temporal encoder, a semantic encoder and a log action decoder, and it fully captures the long-term temporal information in user sessions. Moreover, to bridge the semantic gap between log-trace data and human language, a recurrent semantics memory unit (RSMU) is proposed to encode the annotation sentences from an auxiliary software tutorial dataset, and the output of RSMU is fed into the semantic encoder of Log2Intent. Comprehensive experiments on a real-world Photoshop log-trace dataset with an auxiliary Photoshop tutorial dataset demonstrate the effectiveness of the proposed Log2Intent framework over the state-of-the-art log-trace user modeling method in three different tasks, including log annotation retrieval, user interest detection and user next action prediction.

Understanding user behavior and predicting future behavior on the web is critical for providing seamless user experiences as well as increasing revenue of service providers. Recently, thanks to the remarkable success of recurrent neural networks (RNNs), it has been widely used for modeling sequences of user behaviors. However, although sequential behaviors appear across multiple domains in practice, existing RNN-based approaches still focus on the single-domain scenario assuming that sequential behaviors come from only a single domain. Hence, in order to analyze sequential behaviors across multiple domains, they require to separately train multiple RNN models, which fails to jointly model the interplay among sequential behaviors across multiple domains. Consequently, they often suffer from lack of information within each domain. In this paper, we first introduce a practical but overlooked phenomenon in sequential behaviors across multiple domains, i.e.,domain switch where two successive behaviors belong to different domains. Then, we propose aDomain Switch-Aware Holistic Recurrent Neural Network (DS-HRNN) that effectively shares the knowledge extracted from multiple domains by systematically handlingdomain switch for the multi-domain scenario. DS-HRNN jointly models the multi-domain sequential behaviors and accurately predicts the future behaviors in each domain with only a single RNN model. Our extensive evaluations on two real-world datasets demonstrate that \DCHRNN\ outperforms existing RNN-based approaches and non-sequential baselines with significant improvements by up to 14.93% in terms of recall of the future behavior prediction.

Graph-based semi-supervised learning method has been influential in the data mining and machine learning fields. The key is to construct an effective graph to capture the intrinsic data structure, which further benefits for propagating the unlabeled data over the graph. The existing methods have shown the effectiveness of a graph regularization term on measuring the similarities among samples, which further uncovers the data structure. However, all the existing graph-based methods are on the sample-level, i.e. calculate the similarity based on sample-level representation coefficients, inevitably overlooking the underlying part-level structure within sample. Inspired by the strong interpretability of Non-negative Matrix Factorization (NMF) method, we design a more robust and discriminative graph, by integrating low-rank factorization and graph regularizer into a unified framework. Specifically, a novel low-rank factorization through Semi-Non-negative Matrix Factorization (SNMF) is proposed to extract the semantically part-level representation. Moreover, instead of incorporating a graph regularization on sample-level, we propose a sparse graph regularization term built on the decomposed part-level representation. This practice results in a more accurate measurement among samples, generating a more discriminative graph for semi-supervised learning. As a non-trivial contribution, we also provide an optimization solution to the proposed method. Comprehensive experimental evaluations show that our proposed method is able to achieve superior performance compared with the state-of-the-art semi-supervised classification baselines in both transductive and inductive scenarios.