Reviewerly/exHarmony huggingface dataset:
Toward a Better Understanding of Loss Functions for Collaborative Filtering

Collaborative filtering (CF) is a pivotal technique in modern recommender systems. The learning process of CF models typically consists of three components: interaction encoder, loss function, and negative sampling. Although many existing studies have proposed various CF models to design sophisticated interaction encoders, recent work shows that simply reformulating the loss functions can achieve significant performance gains. This paper delves into analyzing the relationship among existing loss functions. Our mathematical analysis reveals that the previous loss functions can be interpreted as alignment and uniformity functions: (i) the alignment matches user and item representations, and (ii) the uniformity disperses user and item distributions. Inspired by this analysis, we propose a novel loss function that improves the design of alignment and uniformity considering the unique patterns of datasets called Margin-aware Alignment and Weighted Uniformity (MAWU). The key novelty of MAWU is two-fold: (i) margin-aware alignment (MA) mitigates user/item-specific popularity biases, and (ii) weighted uniformity (WU) adjusts the significance between user and item uniformities to reflect the inherent characteristics of datasets. Extensive experimental results show that MF and LightGCN equipped with MAWU are comparable or superior to state-of-the-art CF models with various loss functions on three public datasets.
Improving Graph Domain Adaptation with Network Hierarchy

Graph domain adaptation models have become instrumental in addressing cross-network learning problems due to their ability to transfer abundant label and structural knowledge from source graphs to target graphs. A crucial step in transfer involves measuring domain discrepancy, which refers to distribution shifts between graphs from source and target domains. While conventional models simply provide a node-level measurement, exploiting information from different levels of network hierarchy is intuitive. As each hierarchical level characterizes distinct and meaningful properties or functionalities of the original graph, integrating domain discrepancy based on such hierarchies should contribute to a more precise domain discrepancy measurement. Moreover, class conditional distribution shift is often overlooked in node classification tasks, which could potentially lead to sub-optimal performance. To address the above limitations, we propose a new graph domain adaptation model and apply it to cross-network node classification tasks. Specifically, a hierarchical pooling model to extract meaningful and adaptive hierarchical structures is designed, where both marginal and class conditional distribution shifts on each hierarchical level are jointly minimized. The effectiveness is demonstrated through theoretical analysis and experimental studies across various datasets.
Integrating Priors into Domain Adaptation Based on Evidence Theory

Domain adaptation aims to build up a learning model for target domain by leveraging transferable knowledge from different but related source domains. Existing domain adaptation methods generally transfer the knowledge from source domain to target domain through measuring the consistency between the different domains. Under this strategy, if the data of source domain is not sufficient to guarantee the consistency, the transferable knowledge will be very limited. On the other hand, we often have priors about target domain which facilitate knowledge transfer but are neglected in the extant domain adaptation methods. To tackle the problems, we integrate the priors of target domain into transfer process and propose a domain adaptation method based on evidence evidence theory. We represent the priors with evidential belief function and reformulate the domain adaptation objective based on likelihood principle, in which the priors are used to adjust transferred knowledge to suit for target domain. Based on this, we propose an improved coordinate ascent algorithm to optimize likelihood objective of domain adaption. Experimental results on both text and image datasets validate that the proposed method is effective to improve the knowledge transferability in domain adaptation, especially when the source domain is limited.
MI-DPG: Decomposable Parameter Generation Network Based on Mutual Information for Multi-Scenario Recommendation

Conversion rate (CVR) prediction models play a vital role in recommendation systems. Recent research shows that learning a unified model to serve multiple scenarios is effective for improving overall performance. However, it remains challenging to improve model prediction performance across scenarios at low model parameter cost, and current solutions are hard to robustly model multi-scenario diversity. In this paper, we propose MI-DPG for the multi-scenario CVR prediction, which learns scenario-conditioned dynamic model parameters for each scenario in a more efficient and effective manner. Specifically, we introduce an auxiliary network to generate scenario-conditioned dynamic weighting matrices, which are obtained by combining decomposed scenario-specific and scenario-shared low-rank matrices with parameter efficiency. For each scenario, weighting the backbone model parameters by the weighting matrix helps to specialize the model parameters for different scenarios. It can not only modulate the complete parameter space of the backbone model but also improve the model effectiveness. Furthermore, we design a mutual information regularization to enhance the diversity of model parameters across scenarios by maximizing the mutual information between the scenario-aware input and the scenario-conditioned dynamic weighting matrix. Experiments from three real-world datasets show that MI-DPG outperforms previous multi-scenario recommendation models.
Identify Risky Rules to Reduce Side Effects in Association Rule Hiding

Data sharing is necessary for many practical applications. People do, however, frequently worry about the problem of privacy leaking.This study focuses on preventing the disclosure of sensitive information using association rules and frequent itemsets, which are frequently utilized in numerous applications. How to minimize side effects while hiding, particularly side effects on non-sensitive knowledge, is the difficult part of the problem. The majority of association rule hiding techniques currently in use solely consider reducing side effects on frequent itemsets (patterns), rather than rules, in order to conceal sensitive rules by reducing the statistical disclosure of the itemsets that generate such rules. In this study, we provide a concealment technique utilizing potentially risky rules to lessen adverse impacts on non-sensitive rules, not only itemsets. In addition, this method can be tailored to conceal sensitive itemsets, instead of rules. Extensive experiments show that in most cases the proposed solution can bring fewer side effects on rules, frequent patterns or data quality than existing methods.
Multi-step Prompting for Few-shot Emotion-Grounded Conversations

Conversational systems have shown immense growth in their ability to communicate like humans. With the emergence of large pre-trained language models (PLMs) the ability to provide informative responses have improved significantly. Despite the success of PLMs, the ability to identify and generate engaging and empathetic responses is largely dependent on labelled-data. In this work, we design a prompting approach that identifies the emotion of a given utterance and uses the emotion information for generating the appropriate responses for conversational systems. We propose a two-step prompting method that first recognises the emotion in the dialogue utterance and in the second-step uses the predicted emotion to prompt the PLM to generate the corresponding em- pathetic response in a few-shot setting. Experimental results on three publicly available datasets show that our proposed approach outperforms the state-of-the-art approaches for both automatic and manual evaluation.
Modeling Preference as Weighted Distribution over Functions for User Cold-start Recommendation

=User cold-start recommendation is a well-known challenge in current recommender systems. The cause is that the number of user interactions is too few to accurately estimate user preferences. Furthermore, the uncertainty of user interactions intensifies along with the number of user interactions decreasing. Although existing meta-learning based models with globally sharing knowledge show good performance in most cold-start scenarios, the ability of handling challenges on intention importance and prediction uncertainty is missing: (1) Intra-user uncertainty. When estimating user preferences (reflected in the user's latent representation), each of user interactions is independently considered in the form of user-item pair, which cannot capture the correlation between user interactions, as well as considering the global intent under user interactions. (2) Inter-user importance. During the model training, all users are treated as equally important, which cannot distinguish the contribution of users in the model training process. Assigning the same weight to all users may lead to users with high uncertainty incorrectly guiding the model learning in the early stage of training. To tackle the above challenges, in this paper, we focus on modeling user preference as a weighted distribution over functions (WDoF) for user cold-start recommendation, which not only models the intra-user uncertainty through neural processes with Multinomial likelihood but also considers the importance of different users with curriculum learning during the model training process. Furthermore, we provide a theoretical explanation that why the proposed model performs better than regular neural processes based recommendation methods. Experiments on four real-world datasets demonstrate the effectiveness of the proposed model over several state-of-the-art cold-start recommendation methods.
Personalized Interest Sustainability Modeling for Sequential POI Recommendation

Sequential point-of-interest (POI) recommendation endeavors to capture users' dynamic interests based on their historical check-ins, subsequently predicting the next POIs that they are most likely to visit.Existing methods conventionally capture users' personalized dynamic interests from their chronological sequences of visited POIs. However, these methods fail to explicitly consider personalized interest sustainability, which means whether each user's interest in specific POIs will sustain beyond the training time. In this work, we propose a personalized INterest Sustainability modeling framework for sequential POI REcommendation, INSPIRE for brevity. Different from existing methods that directly recommend next POIs through users' historical trajectories, our proposed INSPIRE focuses on users' personalized interest sustainability. Specifically, we first develop a new task to predict whether each user will visit the POIs in the recent period of the training time. Afterwards, to remedy the sparsity issue of users' check-in history, we propose to augment users' check-in history in three ways: geographical, intrinsic, and extrinsic schemes. Extensive experiments are conducted on two real-world datasets and results show that INSPIRE outperforms existing next POI solutions.
Joint Link Prediction Via Inference from a Model

A Joint Link Prediction Query (JLPQ) specifies a set of links to be predicted, given another set of links as well as node attributes as evidence. While single link prediction has been well studied in literature on deep graph learning, predicting multiple links together has gained little attention. This paper presents a novel framework for computing JLPQs using a probabilistic deep Graph Generative Model. Specifically, we develop inference procedures for an inductively trained Variational Graph Auto-Encoder (VGAE) that estimates the joint link probability for any input JLPQ, without retraining. For evaluation, we apply inference to a range of joint link prediction queries on six benchmark datasets. We find that for most datasets and query types, joint link prediction via inference from a model achieves good predictive performance, better than the independent link prediction baselines (by 0.02-0.4 AUC points depending on the dataset).
Incorporating Constituent Syntax into Grammatical Error Correction with Multi-Task Learning

Grammatical Error Correction (GEC) is usually considered as a translation task where an erroneous sentence is treated as the source language and the corrected sentence as the target language. The state-of-the-art GEC models often adopt transformer-based sequence-to-sequence architecture of machine translation. However, most of these approaches ignore the syntactic information because the syntax of an erroneous sentence is also full of errors and not beneficial to GEC. In this paper, we propose a novel Error-Correction Constituent Parsing (ECCP) task which uses the constituent parsing of corrected sentences to avoid the harmful effect of the erroneous sentence. We also propose an architecture that includes one encoder and two decoders. There are millions of parameters in transformer-based GEC models, and the labeled training data is substantially less than synthetic pre-training data. Therefore, adapter layers are added to the proposed architecture, and adapter tuning is used for fine-tuning our model to alleviate the low-resource issue. We conduct experiments on CoNLL-2014, BEA-2019, and JFLEG test datasets in unsupervised and supervised settings. Experimental results show that our method outperforms the-state-of-art baselines and achieves superior performance on all datasets.
Modeling Sequential Collaborative User Behaviors For Seller-Aware Next Basket Recommendation

Next Basket Recommendation (NBR) aims to recommend a set of products as a basket to users based on their historical shopping behavior. In this paper, we investigate the problem of NBR in online marketplaces (e.g., Instacart, Uber Eats) that connect users with multiple sellers. In such scenarios, effective NBR can significantly enhance the shopping experience of users by recommending diversified and completed products based on specific sellers, especially when a user purchases from a seller they have not visited before. However, conventional NBR approaches assume that all considered products are from the same sellers, which overlooks the complex relationships between users, sellers, and products. To address such limitations, we develop SecGT, a sequential collaborative graph transformer framework that recommends users with baskets from specific sellers based on seller-aware user preference representations that are generated by collaboratively modeling the joint user-seller-product interactions and sequentially exploring the user-agnostic basket transitions in an interactive way. We evaluate the performance of SecGT on users from a leading online marketplace at multiple cities with various involved sellers. The results show that SecGT outperforms existing NBR and also traditional product recommendation approaches on recommending baskets from cold sellers for different types of users across all cities.
Multimodal Optimal Transport Knowledge Distillation for Cross-domain Recommendation

Recommendation systems have been widely used in e-commerce, news media, and short video platforms. With the abundance of images, text, and audio information, users often engage in personalized interactions based on their multimodal preferences. With the continuous expansion of application scenarios, cross domain recommendation issues have become important, such as recommendations in both the public and private domains of e-commerce. The current cross domain recommendation methods have achieved certain results through methods such as shared encoders and contrastive learning. However, few studies have focused on the effective extraction and utilization of multimodal information in cross domain recommendations. Furthermore, due to the existence of distribution drift issues, directly constructing feature alignment between source domain and target domain representations is not an effective way. Therefore, we propose a Multimodal Optimal Transport Knowledge Distillation (MOTKD) method for cross domain recommendation. Specifically, we propose a multimodal graph attention network to model the multimodal preference representation of users. Then, we introduce a proxy distribution space as a bridge between the source and target domains. Based on the common proxy distribution, we utilize the optimal transport method to achieve cross domain knowledge transfer. Further, in order to improve the auxiliary training effect of source domain supervised signals on target domain, we design a multi-level cross domain knowledge distillation module. We conducted extensive experiments on two pairs of cross domain datasets composed of four datasets. The experimental results indicate that our proposed MOTKD method outperforms other state-of-the-art models.
Multi-scale Graph Pooling Approach with Adaptive Key Subgraph for Graph Representations

The recent progress in graph representation learning boosts the development of many graph classification tasks, such as protein classification and social network classification. One of the mainstream approaches for graph representation learning is the hierarchical pooling method. It learns the graph representation by gradually reducing the scale of the graph, so it can be easily adapted to large-scale graphs. However, existing graph pooling methods discard the original graph structure during downsizing the graph, resulting in a lack of graph topological structure. In this paper, we propose a multi-scale graph neural network (MSGNN) model that not only retains the topological information of the graph but also maintains the key-subgraph for better interpretability. MSGNN gradually discards the unimportant nodes and retains the important subgraph structure during the iteration. The key subgraphs are first chosen by experience and then adaptively evolved to tailor specific graph structures for downstream tasks. The extensive experiments on seven datasets show that MSGNN improves the SOTA performance on graph classification and better retains key subgraphs.
MPMRC-MNER: A Unified MRC framework for Multimodal Named Entity Recognition based Multimodal Prompt

Multimodal named entity recognition (MNER) is a vision-language task, which aims to detect entity spans and classify them to corresponding entity types given a sentence-image pair. Existing methods often regard an image as a set of visual objects, trying to explicitly capture the relations between visual objects and entities. However, since visual objects are often not identical to entities in quantity and type, they may suffer the bias introduced by visual objects rather than aid. Inspired by the success of textual prompt-based fine-tuning (PF) approaches in many methods, in this paper, we propose a Multimodal Prompt-based Machine Reading Comprehension based framework to implicit alignment between text and image for improving MNER, namely MPMRC-MNER. Specifically, we transform text-only query in MRC into multimodal prompt containing image tokens and text tokens. To better integrate image tokens and text tokens, we design a prompt-aware attention mechanism for better cross-modal fusion. At last, contrastive learning with two types of contrastive losses is designed to learn more consistent representation of two modalities and reduce noise. Extensive experiments and analyses on two public MNER datasets, Twitter2015 and Twitter2017, demonstrate the better performance of our model against the state-of-the-art methods.
Demonstration of ViTA: Visualizing, Testing and Analyzing Index Advisors

Index advisors have become an essential tool to optimize index selection and accelerate query processing. Various index advisors have been developed in recent years, and comprehensively assessing their performance from multiple aspects is necessary. In this demonstration, we introduce VITA, a user-friendly and informative tool for interactively Visualizing, Testing, and Analyzing index advisors. For a user-given workload, VITA can visualize the main steps of the index selection procedure in ten existing index advisors to facilitate the management of index advisors. Moreover, VITA can assess the index advisor's robustness w.r.t. workload drift by generating testing workloads, i.e., potentially future workloads that may damage the index advisor's performance. Finally, VITA provides a comparative analysis across index advisors on four aspects, including the index advisor's utility (i.e., the ratio of the reduced workload cost), robustness (i.e., the performance under dynamic workload), overhead (i.e., the time to acquire the final configuration), and scalability (i.e., the volume of the enumerated index candidates). Therefore, VITA can thoroughly compare existing index advisors to help users determine the most suitable index advisor that meets their requirements. VITA is now being integrated into the openGauss platform as a plug-in.
ThyExp: An explainable AI-assisted Decision Making Toolkit for Thyroid Nodule Diagnosis based on Ultra-sound Images

Radiologists have an important task of diagnosing thyroid nodules present in ultra sound images. Although reporting systems exist to aid in the diagnosis process, these systems do not provide explanations about the diagnosis results. We present ThyExp -- a web based toolkit for it use by medical professionals, allowing for accurate diagnosis with explanations of thyroid nodules present in ultrasound images utilising artificial intelligence models. The proposed web-based toolkit can be easily incorporated into current medical workflows, and allows medical professionals to have the confidence of a highly accurate machine learning model with explanations to provide supplementary diagnosis data. The solution provides classification results with their probability accuracy, as well as the explanations in the form of presenting the key features or characteristics that contribute to the classification results. The experiments conducted on a real-world UK NHS hospital patient dataset demonstrate the effectiveness of the proposed approach. This toolkit can improve the trust of medical professional to understand the confidence of the model in its predictions. This toolkit can improve the trust of medical professionals in understanding the models reasoning behind its predictions.
Enhancing Repeat-Aware Recommendation from a Temporal-Sequential Perspective

Repeat consumption, such as re-purchasing items and re-listening songs, is a common scenario in daily life. To model repeat consumption, the repeat-aware recommendation has been proposed to predict which item will be re-interacted based on the user-item interactions. In this paper, we investigate various inherent characteristics to enhance the performance of repeat-aware recommendation. Specifically, we explore these characteristics from two aspects: one is from the temporal aspect where we consider the time interval relationship in user behavior sequence; the other is from the sequential aspect where we consider the sequential-level relationship. Our intuition is that both thetemporal pattern andsequential pattern reflect users' intentions of repeat consumption.