Reviewerly/exHarmony huggingface dataset:
Multi-scale Multi-modal Dictionary BERT For Effective Text-image Retrieval in Multimedia Advertising

Visual content in multimedia advertising effectively attracts the customer's attention. Search-based multimedia advertising is a cross-modal retrieval problem. Due to the modal gap between texts and images/videos, cross-modal image/video retrieval is a challenging problem. Recently, multi-modal dictionary BERT has bridged the model gap by unifying the images/videos and texts from different modalities through a multi-modal dictionary. In this work, we improve the multi-modal dictionary BERT by developing a multi-scale multi-modal dictionary and propose a Multi-scale Multi-modal Dictionary BERT (M^2D-BERT). The multi-scale dictionary partitions the feature space into different levels and is effective in describing the fine-level relevance and the coarse-level relevance between the text and images. Meanwhile, we constrain that the code-words in dictionaries from different scales to be orthogonal to each other. Thus, it ensures multiple dictionaries are complementary to each other. Moreover, we adopt a two-level residual quantization to enhance the capacity of each multi-modal dictionary. Systematic experiments conducted on large-scale cross-modal retrieval datasets demonstrate the excellent performance of our M2D-BERT.
Dialogue State Tracking Based on Hierarchical Slot Attention and Contrastive Learning

Dialogue state is a key information in traditional task-oriented dialogue systems, which represents the user's dialogue intention at each moment through a set of (slot, value). The recent methods model the slot and the dialogue context to keep track of the state, but there is a lack of refinement of context information. They do not consider the influence of dialogue context in different scenarios. Our proposed approach utilizes a fine-grained representation of each slot at multiple levels and incorporates an interaction mechanism to obtain a weight of past memory, present utterance and relevance of the slots. Besides, to address the problem that the dialogue utterance is semantically distant from the corresponding slot value, we introduce the contrastive learning to make the utterance embedding mapped under each slot name more suitable with the ground truth value and away from other slot values. This improves the accuracy of mapping between feature space and semantic space. In the predefined ontology-based approaches, our model achieves leading results with both MultiWOZ2.0 and MultiWOZ2.1 datasets.
Personalized Query Suggestion with Searching Dynamic Flow for Online Recruitment

Employing query suggestion techniques to assist users in articulating their needs during online search has become increasingly vital for search engines in an age of exponential information growth. The success of a query suggestion system lies in understanding and modeling user search intent behind each query accurately, which can hardly be achieved without personalization efforts on taking advantage of dynamic user feedback behaviors and rich contextual information. This valuable area, however, has been still largely untapped by current query suggestion systems. In this work, we propose <u>D</u>ynamic <u>S</u>earching <u>F</u>low <u>M</u>odel (DSFM), a query suggestion framework that is capable of modeling and refining user search intent progressively in recruitment scenarios by leveraging a dynamic flow mechanism. Here the concepts of local flow and global flow are introduced to capture the real-time intention of users and the overall influence of a session, respectively. By utilizing rich semantic information contained in resumes and job requirements, DSFM enables the personalization of query suggestions. In addition, weighted contrast learning is introduced into the training process to produce more extensive targeted query samples and partially alleviate the exposure bias. The adoption of attention mechanism allows the selection of the most relevant information to compose the final intention representation. Extensive experimental results on different categories of real-world datasets demonstrate the effectiveness of our proposed approach on the task of query suggestion for online recruitment platforms.
Asymmetrical Context-aware Modulation for Collaborative Filtering Recommendation

Modern learnable collaborative filtering recommendation models generate user and item representations by deep learning methods (e.g. graph neural networks) for modeling user-item interactions. However, most of them may still have unsatisfied performances due to two issues. Firstly, some models assume that the representations of users or items are fixed when modeling interactions with different objects. However, a user may have different interests in different items, and an item may also have different attractions to different users. Thus the representations of users and items should depend on their contexts to some extent. Secondly, existing models learn representations for user and item by symmetrical dual methods which have identical or similar operations. Symmetrical methods may fail to sufficiently and reasonably extract the features of user and item as their interaction data have diverse semantic properties. To address the above issues, a novel model called Asymmetrical context-awaRe modulation for collaBorative filtering REcommendation (ARBRE) is proposed. It adopts simplified GNNs on collaborative graphs to capture homogeneous user preferences and item attributes, then designs two asymmetrical context-aware modulation models to learn dynamic user interests and item attractions, respectively. The learned representations from user domain and item domain are input pair-wisely into 4 Multi-Layer Perceptrons in different combinations to model user-item interactions. Experimental results on three real-world datasets demonstrate the superiority of ARBRE over various state-of-the-arts.
Nonlinear Causal Discovery in Time Series

Recent years have witnessed the proliferation of the Functional Causal Model (FCM) for causal learning due to its intuitive representation and accurate learning results. However, existing FCM-based algorithms suffer from the ubiquitous nonlinear relations in time-series data, mainly because these algorithms either assume linear relationships, or nonlinear relationships with additive noise, or do not introduce additional assumptions but can only identify nonlinear causality between two variables. This paper contributes in particular to a practical FCM-based causal learning approach, which can maintain effectiveness for real-world nonstationary data with general nonlinear relationships and unlimited variable scale.Specifically, the non-stationarity of time series data is first exploited with the nonlinear independent component analysis, to discover the underlying components or latent disturbances. Then, the conditional independence between variables and these components is studied to obtain a relation matrix, which guides the algorithm to recover the underlying causal graph. The correctness of the proposal is theoretically proved, and extensive experiments further verify its effectiveness. To the best of our knowledge, the proposal is the first so far that can fully identify causal relationships under general nonlinear conditions.
Lightweight Unbiased Multi-teacher Ensemble for Review-based Recommendation

Review-based recommender systems (RRS) have received an increasing interest since reviews greatly enhance recommendation quality and interpretability. However, existing RRS suffer from high computational complexity, biased recommendation and poor generalization. The three problems make them inadequate to handle real recommendation scenarios. Previous studies address each issue separately, while none of them consider solving three problems together under a unified framework. This paper presents LUME (a Lightweight Unbiased Multi-teacher Ensemble) for RRS. LUME is a novel framework that addresses the three problems simultaneously. LUME uses multi-teacher ensemble and debiased knowledge distillation to aggregate knowledge from multiple pretrained RRS, and generates a small, unbiased student recommender which generalizes better. Extensive experiments on various real-world benchmarks demonstrate that LUME successfully tackles the three problems and has superior performance than state-of-the-art RRS and knowledge distillation based RS.
AutoQGS

This study investigates the task of knowledge-based question generation (KBQG). Conventional KBQG works generated questions from fact triples in the knowledge graph, which could not express complex operations like aggregation and comparison in SPARQL. Moreover, due to the costly annotation of large-scale SPARQL-question pairs, KBQG from SPARQL under low-resource scenarios urgently needs to be explored. Recently, since the generative pre-trained language models (PLMs) typically trained in natural language (NL)-to-NL paradigm have been proven effective for low-resource generation, e.g., T5 and BART, how to effectively utilize them to generate NL-question from non-NL SPARQL is challenging. To address these challenges, AutoQGS, an auto-prompt approach for low-resource KBQG from SPARQL, is proposed. Firstly, we put forward to generate questions directly from SPARQL for KBQG task to handle complex operations. Secondly, we propose an auto-prompter trained on large-scale unsupervised data to rephrase SPARQL into NL description, smoothing the low-resource transformation from non-NL SPARQL to NL question with PLMs. Experimental results on the WebQuestionsSP, ComlexWebQuestions 1.1, and PathQuestions show that our model achieves state-of-the-art performance, especially in low-resource settings. Furthermore, a corpora of 330k factoid complex question-SPARQL pairs is generated for further KBQG research.
PAVE: Lazy-MDP based Ensemble to Improve Recall of Product Attribute Extraction Models

E-commerce stores face the challenge of missing and inconsistent attribute values in the product detail pages and have to impute them on behalf of their vendors. Traditional approaches formulate the problem of attribute extraction(AE) from product profiles as natural language tasks such as information extraction or text classification. Such models typically operate at high precision but may yield low recall especially on attributes with an open vocabulary due to 1) missing or incorrect information in product profiles, 2) generalization errors due to lack of contextual understanding, and 3) confidence thresholding to operate at high precision. In this work, we present PAVE: Product Attribute Value Ensemble, a novel reinforcement learning model that usesLazy-MDP formalism to solve for low recall by aggregating information from a sequence of product neighbors. We train a policy network usingProximal Policy Optimization that learns to choose the correct value from the sequence. We observe consistent improvement in recall across all open attributes compared to traditionalAE models with an average lift of 10.3% with no drop in precision. Our method surpasses simple aggregation methods like nearest neighbor, majority vote and binary classifier ensembles and even outperformsAE models for closed attributes. Our approach is scalable, robust to noisy product neighbors and generalizes well on unseen attributes.
Inductive Knowledge Graph Reasoning for Multi-batch Emerging Entities

Over the years, reasoning over knowledge graphs (KGs), which aims to infer new conclusions from known facts, has mostly focused on static KGs. The unceasing growth of knowledge in real life raises the necessity to enable the inductive reasoning ability on expanding KGs. Existing inductive work assumes that new entities all emerge once in a batch, which oversimplifies the real scenario that new entities continually appear. This study dives into a more realistic and challenging setting where new entities emerge in multiple batches. We propose a walk-based inductive reasoning model to tackle the new setting. Specifically, a graph convolutional network with adaptive relation aggregation is designed to encode and update entities using their neighboring relations. To capture the varying neighbor importance, we employ a query-aware feedback attention mechanism during the aggregation. Furthermore, to alleviate the sparse link problem of new entities, we propose a link augmentation strategy to add trustworthy facts into KGs. We construct three new datasets for simulating this multi-batch emergence scenario. The experimental results show that our proposed model outperforms state-of-the-art embedding-based, walk-based and rule-based models on inductive KG reasoning.
HySAGE

The recent popularity of edge devices and Artificial Intelligent of Things (AIoT) has driven a new wave of contextual recommendations, such as location based Point of Interest (PoI) recommendations and computing resource-aware mobile app recommendations. In many such recommendation scenarios, contexts are drifting over time. For example, in a mobile game recommendation, contextual features like locations, battery, and storage levels of mobile devices are frequently drifting over time. However, most existing graph-based collaborative filtering methods are designed under the assumption of static features. Therefore, they would require frequent retraining and/or yield graphical models burgeoning in sizes, impeding their suitability for context-drifting recommendations.
Flow-based Perturbation for Cause-effect Inference

A new causal discovery method is introduced to solve the bivariate causal discovery problem. The proposed algorithm leverages the expressive power of flow-based models and tries to learn the complex relationship between two variables. Algorithms have been developed to infer the causal direction according to empirical perturbation errors obtained from an invertible flow-based function. Theoretical results as well as experimental studies are presented to verify the proposed approach. Empirical evaluations demonstrate that our proposed method could outperform baseline methods on both synthetic and real-world datasets.
Efficient Non-sampling Expert Finding

Expert finding aims at seeking potential users to answer new questions in Community Question Answering (CQA) websites. Most existing methods focus on designing matching frameworks between questions and experts, and rely on negative sampling technology for model training. However, sampling would lose lots of useful information about experts and questions, and make these sampling-based methods suffer the bias and non-robust issues, which may lead to an insufficient matching performance for expert findings. In this paper, we propose a novel Efficient Non-sampling Expert Finding model, named ENEF, which could learn accurate representations of questions and experts from whole training data. In our approach, we adopt a rather basic question encoder and a simple matching framework, then an efficient whole-data optimization method is elaborately designed to learn the model parameters without negative sampling with rather a low space and time complexity. Extensive experimental results on four real-world CQA datasets demonstrate that our model ENEF could achieve better performance and faster training efficiency than existing state-of-the-art expert finding methods.
Dynamic Explicit Embedding Representation for Numerical Features in Deep CTR Prediction

Click-Through Rate (CTR) prediction is a key problem in web search, recommendation systems, and online advertising display. Deep CTR models have achieved good performance due to adoption of the feature embedding and interaction. However, most research has focused on learning better feature interactions, with little attention to embedding representation. In this work, we propose a Dynamic Explicit Embedding Representation (DEER) for numerical features in deep CTR prediction, which can provide explicit and dynamic embedding representation for numerical features. The DEER framework is able to discretize numerical features automatically and dynamically, which can overcome the discontinuity problem in the representation of numeric information. Our methods are tested on two public datasets, and the experimental results show DEER can be applied to various deep CTR models, which also improve the performance effectively.
Reinforced Continual Learning for Graphs

Graph Neural Networks (GNNs) have become the backbone for a myriad of tasks pertaining to graphs and similar topological data structures. While many works have been established in domains related to node and graph classification/regression tasks, they mostly deal with a single task. Continual learning on graphs is largely unexplored and existing graph continual learning approaches are limited to the task-incremental learning scenarios. This paper proposes a graph continual learning strategy that combines the architecture-based and memory-based approaches. The structural learning strategy is driven by reinforcement learning, where a controller network is trained in such a way to determine an optimal number of nodes to be added/pruned from the base network when new tasks are observed, thus assuring sufficient network capacities. The parameter learning strategy is underpinned by the concept of Dark Experience replay method to cope with the catastrophic forgetting problem. Our approach is numerically validated with several graph continual learning benchmark problems in both task-incremental learning and class-incremental learning settings. Compared to recently published works, our approach demonstrates improved performance in both the settings. The implementation code can be found at https://github.com/codexhammer/gcl.
STARDOM: Semantic Aware Deep Hierarchical Forecasting Model for Search Traffic Prediction

We study the search traffic forecasting problem for guaranteed search advertising (GSA) application in e-commerce platforms. The consumers express their purchase intents by posing queries to the e-commerce search engine. GSA is a type of guaranteed delivery (GD) advertising strategy, which forecasts the traffic of search queries, and charges the advertisers according to the predicted volumes of search queries the advertisers willing to buy. We employ the time series forecasting method to make the search traffic prediction. Different from existing time series prediction methods, search queries are semantically meaningful, with semantically similar queries possessing similar time series. And they can be grouped according to the brands or categories they belong to, exhibiting hierarchical structures. To fully take advantage of these characteristics, we design a SemanTic AwaRe Deep hierarchical fOrecasting Model (STARDOM for short) which explores the queries' semantic information and the hierarchical structures formed by the queries. Specifically, to exploit hierarchical structure, we propose a reconciliation learning module. It leverages deep learning model to learn the reconciliation relation between the hierarchical series in the latent space automatically, and forces the coherence constraints through a distill reconciliation loss. To exploit semantic information, we propose a semantic representation module and generate semantic aware series embeddings for queries. Extensive experiments are conducted to confirm the effectiveness of the proposed method.
Semi-supervised Continual Learning with Meta Self-training

Continual learning (CL) aims to enhance sequential learning by alleviating the forgetting of previously acquired knowledge. Recent advances in CL lack consideration of the real-world scenarios, where labeled data are scarce and unlabeled data are abundant. To narrow this gap, we focus on semi-supervised continual learning (SSCL). We exploit unlabeled data under limited supervision in the CL setting and demonstrate the feasibility of semi-supervised learning in CL. In this work, we propose a novel method, namely Meta-SSCL, which combines meta-learning with pseudo-labeling and data augmentations to learn a sequence of semi-supervised tasks without catastrophic forgetting. Extensive experiments on CL benchmark text classification datasets show that our method achieves promising results in SSCL.
Cross-Domain Product Search with Knowledge Graph

The notion personalization lies on the core of a real-world product search system, whose aim is to understand the user's search intent in a fine-grained level. The existing solutions mainly achieve this purpose through a coarse-grained semantic matching in terms of the query and item's description or the collective click correlations. Besides the issued query, the historical search behaviors of a user would cover lots of her personalized interests, which is a promising avenue to alleviate the semantic gap between users, items and queries. However, as to a specific domain, a user's search behaviors are generally sparse or even unavailable (i.e., cold-start users). How to exploit the search behaviors from the other relevant domain and enable effective fine-grained intent understanding remains largely unexplored for product search. Moreover, the semantic gap could be further aggravated since the properties of an item could evolve over time (e.g., the price adjustment for a mobile phone or the business plan update for a financial item), which is also mainly overlooked by the existing solutions.
DeepVT

Personalized news recommendation aims to provide people with customized content, which can effectively improve the reading experience. Because user interests in news are diverse and changeable, how to learn accurate user representations is the core challenge in news recommendation. However, most of the previous works only apply news-level representation for user modeling directly, the views of news, such as title, abstract, and category, are only implied and compressed into a single vector of news, which makes it impossible for different views in different news to interact with each other. In this paper, we first focus on the view-level information for user modeling and propose Deep View-Temporal Interaction Network (DeepVT) for news recommendation. It mainly contains two components, i.e., 2D semi-causal convolutional neural network (SC-CNN) and multi-operator attention (MoA). SC-CNN can synthesize interaction information at the view-level and temporal information at the news-level simultaneously and efficiently. And MoA integrates different similarity operators in self-attention functions to avoid attention bias and enhance robustness. By collaboration with SC-CNN, the global interaction at the view-level becomes more sufficient. Experiments on a large-scale real-world dataset, Microsoft News Dataset (MIND), show that our model outperforms previous models in terms of all metrics significantly.
PLASMA: A Semantic Modeling Tool for Domain Experts

In recent years, Knowledge Graphs and Ontology-based Data Management have proven to be particularly effective in the efficient management and consolidation of heterogeneous data sources. In this context, semantic modeling has proven to be a useful approach for creating semantic data annotations. However, automatically generated semantic models usually need to be revised by a domain expert, who is often not familiar with semantic technologies. For addressing this issue, we propose the PLASMA semantic modeling tool, which aims at enabling domain experts to build semantic models from scratch or refine models created by automatic algorithms. We demonstrate the use of the tool and its user interface in two different semantic data management use cases for integrating smart city data in a public funded project, called City Dataspace, and for creating semantic models in an industrial use case at Siemens AG.