Reviewerly/exHarmony huggingface dataset:
An Effective, Efficient, and Scalable Confidence-based Instance Selection Framework for Transformer-Based Text Classification

Transformer-based deep learning is currently the state-of-the-art in many NLP and IR tasks. However, fine-tuning such Transformers for specific tasks, especially in scenarios of ever-expanding volumes of data with constant re-training requirements and budget constraints, is costly (computationally and financially) and energy-consuming. In this paper, we focus on Instance Selection (IS) - a set of methods focused on selecting the most representative documents for training, aimed at maintaining (or improving) classification effectiveness while reducing total time for training (or fine-tuning). We propose E2SC-IS -- Effective, Efficient, and Scalable Confidence-Based IS -- a two-step framework with a particular focus on Transformers and large datasets. E2SC-IS estimates the probability of each instance being removed from the training set based on scalable, fast, and calibrated weak classifiers. E2SC-IS also exploits iterative heuristics to estimate a near-optimal reduction rate. Our solution can reduce the training sets by 29% on average while maintaining the effectiveness in all datasets, with speedup gains up to 70%, scaling for very large datasets (something that the baselines cannot do).
Repetition and Exploration in Sequential Recommendation

In several recommendation scenarios, including next basket recommendation, the importance of repetition and exploration has been discovered and studied. Sequential recommenders (SR) aim to infer a user's preferences and suggest the next item for them to interact with based on their historical interaction sequences. There has not been a systematic analysis of sequential recommenders from the perspective of repetition and exploration. As a result, it is unclear how these models, that are typically optimized for accuracy, perform in terms of repetition and exploration, as well as the potential drawbacks of deploying them in real applications.
DECAF: A Modular and Extensible Conversational Search Framework

The Conversational Search (CS) paradigm allows for an intuitive interaction between the user and the system through natural language sentences and it is increasingly being adopted in various scenarios. However, its widespread experimentation has led to the birth of a multitude of conversational search systems with custom implementations and variants of information retrieval models. This exacerbates the reproducibility crisis already observed in several research areas, including Information Retrieval (IR). To address this issue, we propose DECAF: a modular and extensible conversational search framework designed for fast prototyping and development of conversational agents. Our framework integrates all the components that characterize a modern conversational search system and allows for the seamless integration of Machine Learning (ML) and Large Language Models (LLMs)-based techniques. Furthermore, thanks to its uniform interface, DECAF allows for experiments characterized by a high degree of reproducibility. DECAF contains several state-of-the-art components including query rewriting, search functions under BoW and dense paradigms, and re-ranking functions. Our framework is tested on two well-known conversational collections: TREC CAsT 2019 and TREC CAsT 2020 and the results can be used by future practitioners as baselines. Our contributions include the identification of a series of state-of-the-art components for the conversational search task and the definition of a modular framework for its implementation.
AdaMCL: Adaptive Fusion Multi-View Contrastive Learning for Collaborative Filtering

Graph collaborative filtering has achieved great success in capturing users' preferences over items. Despite effectiveness, graph neural network (GNN)-based methods suffer from data sparsity in real scenarios. Recently, contrastive learning (CL) has been used to address the problem of data sparsity. However, most CL-based methods only leverage the original user-item interaction graph to construct the CL task, lacking the explicit exploitation of the higher-order information (i.e., user-user and item-item relationships). Even for the CL-based method that uses the higher-order information, the reception field of the higher-order information is fixed and regardless of the difference between nodes. In this paper, we propose a novel adaptive multi-view fusion contrastive learning framework, named AdaMCL, for graph collaborative filtering. To exploit the higher-order information more accurately, we propose an adaptive fusion strategy to fuse the embeddings learned from the user-item and user-user graphs. Moreover, we propose a multi-view fusion contrastive learning paradigm to construct effective CL tasks. Besides, to alleviate the noisy information caused by aggregating higher-order neighbors, we propose a layer-level CL task. Extensive experimental results reveal that AdaMCL is effective and outperforms existing collaborative filtering models significantly.
RCENR: A Reinforced and Contrastive Heterogeneous Network Reasoning Model for Explainable News Recommendation

Existing news recommendation methods suffer from sparse and weak interaction data, leading to reduced effectiveness and explainability. Knowledge reasoning, which explores inferential trajectories in the knowledge graph, can alleviate data sparsity and provide explicitly recommended explanations. However, brute-force pre-processing approaches used in conventional methods are not suitable for fast-changing news recommendation. Therefore, we propose an explainable news recommendation model: the Reinforced and Contrastive Heterogeneous Network Reasoning Model for Explainable News Recommendation (RCENR), consisting of NHN-R2 and MR&CO frameworks. The NHN-R2 framework generates user/news subgraphs to enhance recommendation and extend the dimensions and diversity of reasoning. The MR&CO framework incorporates contrastive learning with a reinforcement-based strategy for self-supervised and efficient model training. Experiments on the MIND dataset show that RCENR is able to improve recommendation accuracy and provide diverse and credible explanations.
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer

Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval. Though recent deep learning methods can partially solve the problem, they often fail to handle the numerous sparse features, particularly those tail feature values with infrequent occurrences in the training data. Worse still, existing methods cannot explicitly leverage the correlations among different instances to help further improve the representation learning on sparse features since such relational prior knowledge is not provided. To address these challenges, in this paper, we tackle the problem of representation learning on feature-sparse data from a graph learning perspective. Specifically, we propose to model the sparse features of different instances using hypergraphs where each node represents a data instance and each hyperedge denotes a distinct feature value. By passing messages on the constructed hypergraphs based on our Hypergraph Transformer (HyperFormer), the learned feature representations capture not only the correlations among different instances but also the correlations among features. Our experiments demonstrate that the proposed approach can effectively improve feature representation learning on sparse features.
Adapting Generative Pretrained Language Model for Open-domain Multimodal Sentence Summarization

Multimodal sentence summarization, aiming to generate a brief summary of the source sentence and image, is a new yet challenging task. Although existing methods have achieved compelling success, they still suffer from two key limitations: 1) lacking the adaptation of generative pre-trained language models for open-domain MMSS, and 2) lacking the explicit critical information modeling. To address these limitations, we propose a BART-MMSS framework, where BART is adopted as the backbone. To be specific, we propose a prompt-guided image encoding module to extract the source image feature. It leverages several soft to-be-learned prompts for image patch embedding, which facilitates the visual content injection to BART for open-domain MMSS tasks. Thereafter, we devise an explicit source critical token learning module to directly capture the critical tokens of the source sentence with the reference of the source image, where we incorporate explicit supervision to improve performance. Extensive experiments on a public dataset fully validate the superiority of our proposed method. In addition, the predicted tokens by the vision-guided key-token highlighting module can be easily understood by humans and hence improve the interpretability of our model.
Adapting Learned Sparse Retrieval for Long Documents

Learned sparse retrieval (LSR) is a family of neural retrieval methods that transform queries and documents into sparse weight vectors aligned with a vocabulary. While LSR approaches like Splade work well for short passages, it is unclear how well they handle longer documents. We investigate existing aggregation approaches for adapting LSR to longer documents and find that proximal scoring is crucial for LSR to handle long documents. To leverage this property, we proposed two adaptations of the Sequential Dependence Model (SDM) to LSR: ExactSDM and SoftSDM. ExactSDM assumes only exact query term dependence, while SoftSDM uses potential functions that model the dependence of query terms and their expansion terms (i.e., terms identified using a transformer's masked language modeling head).
ranxhub: An Online Repository for Information Retrieval Runs

ranxhub is an online repository for sharing artifacts deriving from the evaluation of Information Retrieval systems. Specifically, we provide a platform for sharing pre-computed runs: the ranked lists of documents retrieved for a specific set of queries by a retrieval model. We also extend ranx, a Python library for the evaluation and comparison of Information Retrieval runs, adding functionalities to integrate the usage of ranxhub seamlessly, allowing the user to compare the results of multiple systems in just a few lines of code. In this paper, we first outline the many advantages and implications that an online repository for sharing runs can bring to the table. Then, we introduce ranxhub and its integration with ranx, showing its very simple usage. Finally, we discuss some use cases for which ranxhub can be highly valuable for the research community.
Intent-aware Ranking Ensemble for Personalized Recommendation

Ranking ensemble is a critical component in real recommender systems. When a user visits a platform, the system will prepare several item lists, each of which is generally from a single behavior objective recommendation model. As multiple behavior intents, e.g., both clicking and buying some specific item category, are commonly concurrent in a user visit, it is necessary to integrate multiple single-objective ranking lists into one. However, previous work on rank aggregation mainly focused on fusing homogeneous item lists with the same objective while ignoring ensemble of heterogeneous lists ranked with different objectives with various user intents.
SONAR: Web-based Tool for Multimodal Exploration of Non-Fungible Token Inspiration Networks

In this work, we present SONAR, a web-based tool for multimodal exploration of Non-Fungible Token (NFT) inspiration networks. SONAR is conceived to support both creators and traders in the emerging Web3 by providing an interactive visualization of the inspiration-driven connections between NFTs, at both individual level and collection level. SONAR can hence be useful to identify new investment opportunities as well as anomalous inspirations. To demonstrate SONAR's capabilities, we present an application to the largest and most representative dataset concerning the NFT landscape to date, showing how our proposed tool can scale and ensure high-level user experience up to millions of edges.
Multimodal Counterfactual Learning Network for Multimedia-based Recommendation

Multimedia-based recommendation (MMRec) utilizes multimodal content (images, textual descriptions, etc.) as auxiliary information on historical interactions to determine user preferences. Most MMRec approaches predict user interests by exploiting a large amount of multimodal contents of user-interacted items, ignoring the potential effect of multimodal content of user-uninteracted items. As a matter of fact, there is a small portion of user preference-irrelevant features in the multimodal content of user-interacted items, which may be a kind of spurious correlation with user preferences, thereby degrading the recommendation performance. In this work, we argue that the multimodal content of user-uninteracted items can be further exploited to identify and eliminate the user preference-irrelevant portion inside user-interacted multimodal content, for example by counterfactual inference of causal theory. Going beyond multimodal user preference modeling only using interacted items, we propose a novel model called Multimodal Counterfactual Learning Network (MCLN), in which user-uninteracted items' multimodal content is additionally exploited to further purify the representation of user preference-relevant multimodal content that better matches the user's interests, yielding state-of-the-art performance. Extensive experiments are conducted to validate the effectiveness and rationality of MCLN. We release the complete codes of MCLN at https://github.com/hfutmars/MCLN.
Surface Geometry Processing: An Efficient Normal-Based Detail Representation

With the rapid development of high-resolution 3D vision applications, the traditional way of manipulating surface detail requires considerable memory and computing time. To address these problems, we introduce an efficient surface detail processing framework in 2D normal domain, which extracts new normal feature representations as the carrier of micro geometry structures that are illustrated both theoretically and empirically in this article. Compared with the existing state of the arts, we verify and demonstrate that the proposed normal-based representation has three important properties, including detail separability, detail transferability and detail idempotence. Finally, three new schemes are further designed for geometric surface detail processing applications, including geometric texture synthesis, geometry detail transfer, and 3D surface super-resolution. Theoretical analysis and experimental results on the latest benchmark dataset verify the effectiveness and versatility of our normal-based representation, which accepts 30 times of the input surface vertices but at the same time only takes 6.5% memory cost and 14.0% running time in comparison with existing competing algorithms.
OpenMatch-v2: An All-in-one Multi-Modality PLM-based Information Retrieval Toolkit

Pre-trained language models (PLMs) have emerged as the foundation of the most advanced Information Retrieval (IR) models. Powered by PLMs, the latest IR research has proposed novel models, new domain adaptation algorithms as well as enlarged datasets. In this paper, we present a Python-based IR toolkit OpenMatch-v2. As a full upgrade of OpenMatch proposed in 2021, OpenMatch-v2 incorporates the most recent advancements of PLM-based IR research, providing support for new, cross-modality models and enhanced domain adaptation techniques with a streamlined, optimized infrastructure. The code of OpenMatch is publicly available at https://github.com/OpenMatch/OpenMatch.
Weighted Knowledge Graph Embedding

Knowledge graph embedding (KGE) aims to project both entities and relations in a knowledge graph (KG) into low-dimensional vectors. Indeed, existing KGs suffer from the data imbalance issue, i.e., entities and relations conform to a long-tail distribution, only a small portion of entities and relations occur frequently, while the vast majority of entities and relations only have a few training samples. Existing KGE methods assign equal weights to each entity and relation during the training process. Under this setting, long-tail entities and relations are not fully trained during training, leading to unreliable representations. In this paper, we propose WeightE, which attends differentially to different entities and relations. Specifically, WeightE is able to endow lower weights to frequent entities and relations, and higher weights to infrequent ones. In such manner, WeightE is capable of increasing the weights of long-tail entities and relations, and learning better representations for them. In particular, WeightE tailors bilevel optimization for the KGE task, where the inner level aims to learn reliable entity and relation embeddings, and the outer level attempts to assign appropriate weights for each entity and relation. Moreover, it is worth noting that our technique of applying weights to different entities and relations is general and flexible, which can be applied to a number of existing KGE models. Finally, we extensively validate the superiority of WeightE against various state-of-the-art baselines.
Distributionally Robust Sequential Recommnedation

Modeling user sequential behaviors have been demonstrated to be effective in promoting the recommendation performance. While previous work has achieved remarkable successes, they mostly assume that the training and testing distributions are consistent, which may contradict with the diverse and complex user preferences, and limit the recommendation performance in real-world scenarios. To alleviate this problem, in this paper, we propose a robust sequential recommender framework to overcome the potential distribution shift between the training and testing sets. In specific, we firstly simulate different training distributions via sample reweighting. Then, we minimize the largest loss induced by these distributions to optimize the 'worst-case' loss for improving the model robustness. Considering that there can be too many sample weights, which may introduce too much flexibility and be hard to optimize, we cluster the training samples based on both hard and soft strategies, and assign each cluster with a unified weight. At last, we analyze our framework by presenting the generalization error bound of the above minimax objective, which help us to better understand the proposed framework from the theoretical perspective. We conduct extensive experiments based on three real-world datasets to demonstrate the effectiveness of our proposed framework. To reproduce our experiments and promote this research direction, we have released our project at https://anonymousrsr.github.io/RSR/.
Data-Aware Proxy Hashing for Cross-modal Retrieval

Recently, numerous proxy hash code based methods, which sufficiently exploit the label information of data to supervise the training of hashing models, have been proposed. Although these methods have made impressive progress, their generating processes of proxy hash codes are based only on the class information of the dataset or labels of data but do not take the data themselves into account. Therefore, these methods will probably generate some inappropriate proxy hash codes, thus damaging the retrieval performance of the hash models. To solve the aforementioned problem, we propose a novel Data-Aware Proxy Hashing for cross-modal retrieval, called DAPH. Specifically, our proposed method first train a data-aware proxy network that takes the data points, label vectors of data, and the class vectors of the dataset as inputs to generate class-based data-aware proxy hash codes, label-fused image-aware proxy hash codes and label-fused text-aware proxy hash codes. Then, we propose a novel hash loss that exploits the three types of data-aware proxy hash codes to supervise the training of modality-specific hashing networks. After training, DAPH is able to generate discriminate hash codes with the semantic information preserved adequately. Extensive experiments on three benchmark datasets show that the proposed DAPH outperforms the state-of-the-art baselines in cross-modal retrieval tasks.
XpmIR: A Modular Library for Learning to Rank and Neural IR Experiments

During past years, several frameworks for (Neural) Information Retrieval have been proposed. However, while they allow reproducing already published results, it is still very hard to re-use some parts of the learning pipelines, such as for instance the pre-training, sampling strategy, or a loss in newly developed models. It is also difficult to use new training techniques with old models, which makes it more difficult to assess the usefulness of ideas on various neural IR models. This slows the adoption of new techniques, and in turn, the development of the IR field. In this paper, we present XpmIR, a Python library defining a reusable set of experimental components. The library already contains state-of-the-art models and indexation techniques and is integrated with the HuggingFace hub.
Time-interval Aware Share Recommendation via Bi-directional Continuous Time Dynamic Graphs

Dynamic share recommendation, which aims at recommending a friend who would like to share a particular item at a certain timestamp, has emerged as a novel task for social-oriented e-commerce platforms. Different from traditional graph-based recommendation tasks, with integrating the interconnected social interactions and fine-grained temporal information from historical share records, this novel task may encounter one unique challenge, i.e., how to deal with the dynamic social connections and asymmetric share interactions. Even worse, users may keep inactive during some periods, which results in difficulties in updating personalized profiles. To address the above challenges, in this paper, we propose a dynamic graph share recommendation model called DynShare. Specifically, we first divide each user embedding into two parts, namely the invitation embedding and vote embedding to show the tendencies of sending and receiving items, respectively. Then, temporal graph attention networks (TGATs) based on bi-directional continuous time dynamic graphs (CTDGs) are leveraged to encode temporal neighbor information from different directions. Afterward, to estimate how different users perceive the time intervals after the last interaction, we further design a time-interval aware personalized projection operator on the foundation of temporal point processes (TPPs) to project user embedding for the next-time share prediction. Extensive experiments on a real-world e-commerce share dataset have demonstrated that our proposed DynShare can achieve better results compared with state-of-the-art baseline methods. And our code is available on the project website: https://github.com/meteor-gif/DynShare.
Context-Aware Modeling via Simulated Exposure Page for CTR Prediction

Click-through rate (CTR) prediction plays a crucial role in industrial recommendation and advertising systems, which generate and expose multiple items for each user request. Although the user's click action on an item will be affected by the other exposed items (called contextual items), current CTR prediction methods do not exploit this context because CTR prediction is performed before the contextual items are generated. This paper introduces a solution Contextual Items Simulation and Modeling (CISM) to tackle this limitation. Specifically, we propose a near-line Context Simulation Center to simulate exposure page without affecting online service latency, and an online Context Modeling Transformer to learn user-wise context from the simulated results w.r.t. the candidate item. In addition, knowledge distillation is introduced to further improve CTR prediction. Extensive experiments on both public and industrial datasets demonstrate the effectiveness of CISM. Currently, CISM has been deployed in the online display advertising system of Meituan Waimai, serving the main traffic.