Reviewerly/exHarmony huggingface dataset:
Distillation-Enhanced Graph Masked Autoencoders for Bundle Recommendation

Bundle recommendation aims to recommend a bundle of items to users as a whole with user-bundle (U-B) interaction information, and auxiliary user-item (U-I) interaction and bundle-item affiliation information. Recent methods usually use two graph neural networks (GNNs) to model user's bundle preferences separately from the U-B graph (bundle view) and U-I graph (item view). However, by conducting statistical analysis, we find that the auxiliary U-I information is far underexplored due to the following reasons: 1) Loosely combining the predicted results cannot well synthesize the knowledge from both views. 2) The local U-B and U-I collaborative relations might not be consistent, leading to GNN's inaccurate modeling of user's bundle preference from the U-I graph. 3) The U-I interactions are usually modeled equally while the significant ones corresponding to user's bundle preference are less emphasized.
Evaluating Task-oriented Dialogue Systems with Users

Evaluation is one of the major concerns when developing information retrieval systems. Especially in the field of conversational AI, this topic has been heavily studied in the setting of both non-task and task-oriented conversational agents (dialogue systems).[1] Recently, several automatic metrics e.g., BLEU and ROUGE, proposed for the evaluation of dialogue systems, have shown poor correlation with human judgment and are thus ineffective for the evaluation of dialogue systems. As a consequence, a significant amount of research relies on human evaluation to estimate the effectiveness of dialogue systems[1, 4}.
Uncertainty Quantification for Extreme Classification

Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making. However, most research in this domain has only focused on problems with small label spaces and ignored eXtreme Multi-label Classification (XMC), which is an essential task in the era of big data for web-scale machine learning applications. Moreover, enormous label spaces could also lead to noisy retrieval results and intractable computational challenges for uncertainty quantification. In this paper, we aim to investigate general uncertainty quantification approaches for tree-based XMC models with a probabilistic ensemble-based framework. In particular, we analyze label-level and instance-level uncertainty in XMC, and propose a general approximation framework based on beam search to efficiently estimate the uncertainty with a theoretical guarantee under long-tail XMC predictions. Empirical studies on six large-scale real-world datasets show that our framework not only outperforms single models in predictive performance, but also can serve as strong uncertainty-based baselines for label misclassification and out-of-distribution detection, with significant speedup. Besides, our framework can further yield better state-of-the-art results based on deep XMC models with uncertainty quantification.
Dataset Preparation for Arbitrary Object Detection: An Automatic Approach based on Web Information in English

Automatic dataset preparation can help users avoid labor-intensive and costly manual data annotations. The difficulty in preparing a high-quality dataset for object detection involves three key aspects: relevance, naturality, and balance, which are not addressed by existing works. In this paper, we leverage information from the web, and propose a fully-automatic dataset preparation mechanism without any human annotation, which can automatically prepare a high-quality training dataset for the detection task with English text terms describing target objects. It contains three key designs, i.e., keyword expansion, data de-noising, and data balancing. Our experiments demonstrate that the object detectors trained with auto-prepared data are comparable to those trained with benchmark datasets and outperform other baselines. We also demonstrate the effectiveness of our approach in several more challenging real-world object categories that are not included in the benchmark datasets.
Understand the Dynamic World: An End-to-End Knowledge Informed Framework for Open Domain Entity State Tracking

Open domain entity state tracking aims to predict reasonable state changes of entities (i.e., [attribute] of [entity] was [before_state] and [after_state] afterwards) given the action descriptions. It's important to many reasoning tasks to support human everyday activities. However, it's challenging as the model needs to predict an arbitrary number of entity state changes caused by the action while most of the entities are implicitly relevant to the actions and their attributes as well as states are from open vocabularies. To tackle these challenges, we propose a novel end-to-end Knowledge Informed framework for open domain Entity State Tracking, namely KIEST, which explicitly retrieves the relevant entities and attributes from external knowledge graph (i.e., ConceptNet) and incorporates them to autoregressively generate all the entity state changes with a novel dynamic knowledge grained encoder-decoder framework. To enforce the logical coherence among the predicted entities, attributes, and states, we design a new constraint decoding strategy and employ a coherence reward to improve the decoding process. Experimental results show that our proposed KIEST framework significantly outperforms the strong baselines on the public benchmark dataset - OpenPI
A Retrieval System for Images and Videos based on Aesthetic Assessment of Visuals

Attractive images or videos are the visual backbones of journalism and social media to gain the user's attention. From trailers to teaser images to image galleries, appealing visuals have only grown in importance over the years. However, selecting eye-catching shots from a video or the perfect image from large image collections is a challenging and time-consuming task. We present our tool that can assess image and video content from an aesthetic standpoint. We discovered that it is possible to perform such an assessment by combining expert knowledge with data-driven information. We combine the relevant aesthetic features and machine learning algorithms into an aesthetics retrieval system, which enables users to sort uploaded visuals based on an aesthetic score and interact with additional photographic, cinematic, and person-specific features.
Unsupervised Story Discovery from Continuous News Streams via Scalable Thematic Embedding

Unsupervised discovery of stories with correlated news articles in real-time helps people digest massive news streams without expensive human annotations. A common approach of the existing studies for unsupervised online story discovery is to represent news articles with symbolic- or graph-based embedding and incrementally cluster them into stories. Recent large language models are expected to improve the embedding further, but a straightforward adoption of the models by indiscriminately encoding all information in articles is ineffective to deal with text-rich and evolving news streams. In this work, we propose a novel thematic embedding with an off-the-shelf pretrained sentence encoder to dynamically represent articles and stories by considering their shared temporal themes. To realize the idea for unsupervised online story discovery, a scalable framework USTORY is introduced with two main techniques, theme- and time-aware dynamic embedding and novelty-aware adaptive clustering, fueled by lightweight story summaries. A thorough evaluation with real news data sets demonstrates that USTORY achieves higher story discovery performances than baselines while being robust and scalable to various streaming settings.
PEPO: Petition Executing Processing Optimizer Based on Natural Language Processing

In this paper, we propose "Petition Executing Process Optimizer (PEPO)," an AI-based petition processing system that features three components, (a) Department Classification, (b) Importance Assessment, and (c) Response Generation for improving the Public Work Bureau (PWB) 1999 Hotline petitions handling process in Taiwan. Our Department Classification algorithm has been evaluated with NDCG, achieving an impressive score of 86.48%, while the Important Assessment function has an accuracy rate of 85%. Besides, Response Generation enhances communication efficiency between the government and citizens. The PEPO system has been deployed as an online web service for the Public Works Bureau of the Tainan City Government. With PEPO, the PWB benefits greatly from the effectiveness and efficiency of handling citizens' petitions.
Continuous Input Embedding Size Search For Recommender Systems

Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance. Latent factor models represent users and items as real-valued embedding vectors for pairwise similarity computation, and all embeddings are traditionally restricted to a uniform size that is relatively large (e.g., 256-dimensional). With the exponentially expanding user base and item catalog in contemporary e commerce, this design is admittedly becoming memory-inefficient. To facilitate lightweight recommendation, reinforcement learning (RL) has recently opened up opportunities for identifying varying embedding sizes for different users/items. However, challenged by search efficiency and learning an optimal RL policy, existing RL-based methods are restricted to highly discrete, predefined embedding size choices. This leads to a largely overlooked potential of introducing finer granularity into embedding sizes to obtain better recommendation effectiveness under a given memory budget. In this paper, we propose continuous input embedding size search (CIESS), a novel RL-based method that operates on a continuous search space with arbitrary embedding sizes to choose from. In CIESS, we further present an innovative random walk-based exploration strategy to allow the RL policy to efficiently explore more candidate embedding sizes and converge to a better decision. CIESS is also model-agnostic and hence generalizable to a variety of latent factor RSs, whilst experiments on two real-world datasets have shown state-of-the-art performance of CIESS under different memory budgets when paired with three popular recommendation models.
Contrastive Box Embedding for Collaborative Reasoning

Most of the existing personalized recommendation methods predict the probability that one user might interact with the next item by matching their representations in the latent space. However, as a cognitive task, it is essential for an impressive recommender system to acquire the cognitive capacity rather than to decide the users' next steps by learning the pattern from the historical interactions through matching-based objectives. Therefore, in this paper, we propose to model the recommendation as a logical reasoning task which is more in line with an intelligent recommender system. Different from the prior works, we embed each query as a box rather than a single point in the vector space, which is able to model sets of users or items enclosed and logical operators (e.g., intersection) over boxes in a more natural manner. Although modeling the logical query with box embedding significantly improves the previous work of reasoning-based recommendation, there still exist two intractable issues including aggregation of box embeddings and training stalemate in critical point of boxes. To tackle these two limitations, we propose a Contrastive Box learning framework for Collaborative Reasoning (CBox4CR). Specifically, CBox4CR combines a smoothed box volume-based contrastive learning objective with the logical reasoning objective to learn the distinctive box representations for the user's preference and the logical query based on the historical interaction sequence. Extensive experiments conducted on four publicly available datasets demonstrate the superiority of our CBox4CR over the state-of-the-art models in recommendation task.
StreamE: Learning to Update Representations for Temporal Knowledge Graphs in Streaming Scenarios

Learning representations for temporal knowledge graphs (TKGs) is a fundamental task. Most existing methods regard TKG as a sequence of static snapshots and recurrently learn representations by retracing the previous snapshots. However, new knowledge can be continuously accrued to TKGs as streams. These methods either cannot handle new entities or fail to update representations in real time, making them unfeasible to adapt to the streaming scenarios. In this paper, we propose a lightweight framework called StreamE towards the efficient generation of TKG representations in streaming scenarios. To reduce the parameter size, entity representations in StreamE are decoupled from the model training to serve as the memory module to store the historical information of entities. To achieve efficient update and generation, the process of generating representations is decoupled as two functions in StreamE. An update function is learned to incrementally update entity representations based on the newly-arrived knowledge and a read function is learned to predict the future semantics of entity representations. The update function avoids the recurrent modeling paradigm and thus gains high efficiency while the read function considers multiple semantic change properties. We further propose a joint training strategy with two temporal regularizations to effectively optimize the framework. Experimental results show that StreamE can achieve better performance than baseline methods with 100x faster in inference, 25x faster in training, and only 1/5 parameter size, which demonstrates its superiority. Code is available at https://github.com/zjs123/StreamE.
Subgraph Search over Neural-Symbolic Graphs

In this paper, we propose neural-symbolic graph databases (NSGDs) that extends traditional graph data with content and structural embeddings in every node. The content embeddings can represent unstructured data (e.g., images, videos, and texts), while structural embeddings can be used to deal with incomplete graphs. We can advocate machine learning models (e.g., deep learning) to transform unstructured data and graph nodes to these embeddings. NSGDs can support a wide range of applications (e.g., online recommendation and natural language question answering) in social-media networks, multi-modal knowledge graphs and etc. As a typical search over graphs, we study subgraph search over a large NSGD, called neural-symbolic subgraph matching (NSMatch) that includes a novel ranking search function. Specifically, we develop a general algorithmic framework to process NSMatch efficiently. Using real-life multi-modal graphs, we experimentally verify the effectiveness, scalability and efficiency of NSMatch.
Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketing

Multi-task learning for various real-world applications usually involves tasks with logical sequential dependence. For example, in online marketing, the cascade behavior pattern of impression \rightarrow click \rightarrow conversion is usually modeled as multiple tasks in a multi-task manner, where the sequential dependence between tasks is simply connected with an explicitly defined function or implicitly transferred information in current works. These methods alleviate the data sparsity problem for long-path sequential tasks as the positive feedback becomes sparser along with the task sequence. However, the error accumulation and negative transfer will be a severe problem for downstream tasks. Especially, at the beginning stage of training, the optimization for parameters of former tasks is not converged yet, and thus the information transferred to downstream tasks is negative. In this paper, we propose a prior information merged model (PIMM), which explicitly models the logical dependence among tasks with a novel prior information merged (PIM) module for multiple sequential dependence task learning in a curriculum manner. Specifically, the PIM randomly selects the true label information or the prior task prediction with a soft sampling strategy to transfer to the downstream task during the training. Following an easy-to-difficult curriculum paradigm, we dynamically adjust the sampling probability to ensure that the downstream task will get the effective information along with the training. The offline experimental results on both public and product datasets verify that PIMM outperforms state-of-the-art baselines. Moreover, we deploy the PIMM in a large-scale FinTech platform, and the online experiments also demonstrate the effectiveness of PIMM.
KATIE: A System for Key Attributes Identification in Product Knowledge Graph Construction

We present part of Huawei's efforts in building a Product Knowledge Graph (PKG). We want to identify which product attributes (i.e. properties) are relevant and important in terms of shopping decisions to product categories (i.e. classes). This is particularly challenging when the attributes and their values are mined from online product catalogues, i.e. HTML pages. These web pages contain semi-structured data, which do not follow a concerted format and use diverse vocabulary to designate the same features. We propose a system for key attribute identification (KATIE) based on fine-tuning pre-trained models (e.g., DistilBERT) to predict the applicability and importance of an attribute to a category. We also propose an attribute synonyms identification module that allows us to discover synonymous attributes by considering not only their labels' similarities but also the similarity of their values sets. We have evaluated our approach to Huawei categories taxonomy and a set of internally mined attributes from web pages. KATIE guarantees promising performance results compared to the most recent baselines.
Dirichlet process mixture models using matrix‐generalized half‐t distribution

Dirichlet process mixture models (or mixture of Dirichlet process [MDP]) are Bayesian non‐parametric mixture models that can solve the problem of determining the number of components in mixture models by assuming infinitely many components. We propose a new approach for MDP by using a matrix‐generalized half‐ distribution as a non‐informative prior for the covariance in the base distribution. This new approach aims to improve the performance of MDP in cases where conjugacy constraints cause underperformance of current priors, such as Wishart distribution or inverse‐Wishart. The effectiveness of this approach is demonstrated through simulations and by comparing its performance on the Old Faithful geyser dataset with existing MDP specifications.
MEME: Multi-Encoder Multi-Expert Framework with Data Augmentation for Video Retrieval

Text-to-video(T2V) retrieval aims to find relevant videos from text queries. The recently introduced Contrastive Language Image Pretraining (CLIP), a pretrained language-vision model trained on large-scale image and caption pairs, has been extensively studied in the literature for this task. Existing studies on T2V task have aimed to transfer the CLIP knowledge and focus on enhancing retrieval performance through fine-grained representation learning. While fine-grained contrast has achieved some remarkable results, less attention has been paid to coarse-grained contrasts. To this end, we propose a method called Graph Patch Spreading (GPS) to aggregate patches across frames at the coarse-grained level. We apply GPS to our proposed framework called Multi-Encoder Multi-Expert (MEME) framework. Our proposed scheme is general enough to be applied to any existing CLIP-based video-text retrieval models. We demonstrate the effectiveness of our method on existing models over the benchmark datasets MSR-VTT, MSVD, and LSMDC datasets. Our code can be found at https://github.com/kang7734/MEME__.
Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation

In dynamic interaction graphs, user-item interactions usually follow heterogeneous patterns, represented by different structural information, such as user-item co-occurrence, sequential information of user interactions and the transition probabilities of item pairs. However, the existing methods cannot simultaneously leverage all three structural information, resulting in suboptimal performance. To this end, we propose øurs, a triple structural information modeling method for accurate, explainable and interactive recommendation on dynamic interaction graphs. Specifically, øurs consists of 1) a dynamic ideal low-pass graph filter to dynamically mine co-occurrence information in user-item interactions, which is implemented by incremental singular value ecomposition (SVD); 2) a parameter-free attention module to capture sequential information of user interactions effectively and efficiently; and 3) an item transition matrix to store the transition probabilities of item pairs. Then, we fuse the predictions from the triple structural information sources to obtain the final recommendation results. By analyzing the relationship between the SVD-based and the recently emerging graph signal processing (GSP)-based collaborative filtering methods, we find that the essence of SVD is an ideal low-pass graph filter, so that the interest vector space in øurs can be extended to achieve explainable and interactive recommendation, making it possible for users to actively break through the information cocoons. Experiments on six public datasets demonstrated the effectiveness of øurs in accuracy, explainability and interactivity.
A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data

The substitute-based recommendation is widely used in E-commerce to provide better alternatives to customers. However, existing research typically uses customer behavior signals like co-view and view-but-purchase-another to capture the substitute relationship. Despite its intuitive soundness, such an approach might ignore the functionality and characteristics of products. In this paper, we adapt substitute recommendations into language matching problem. It takes the product title description as model input to consider product functionality. We design a new transformation method to de-noise the signals derived from production data. In addition, we consider multilingual support from the engineering point of view. Our proposed end-to-end transformer-based model achieves both successes from offline and online experiments. The proposed model has been deployed in a large-scale E-commerce website for 11 marketplaces in 6 languages. Our proposed model is demonstrated to increase revenue by 19% based on an online A/B experiment.
Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation

Recommendation models are typically trained on observational user interaction data, but the interactions between latent factors in users' decision-making processes lead to complex and entangled data. Disentangling these latent factors to uncover their underlying representation can improve the robustness, interpretability, and controllability of recommendation models. This paper introduces the Causal Disentangled Variational Auto-Encoder (CaD-VAE), a novel approach for learning causal disentangled representations from interaction data in recommender systems. The CaD-VAE method considers the causal relationships between semantically related factors in real-world recommendation scenarios, rather than enforcing independence as in existing disentanglement methods. The approach utilizes structural causal models to generate causal representations that describe the causal relationship between latent factors. The results demonstrate that CaD-VAE outperforms existing methods, offering a promising solution for disentangling complex user behavior data in recommendation systems.