Reviewerly/exHarmony huggingface dataset:
Federated News Recommendation with Fine-grained Interpolation and Dynamic Clustering

Researchers have successfully adapted the privacy-preserving Federated Learning (FL) to news recommendation tasks to better protect users' privacy, although typically at the cost of performance degradation due to the data heterogeneity issue. To address this issue, Personalized Federated Learning (PFL) has emerged, among which model interpolation is a promising approach that interpolates the local personalized models with the global model. However, the existing model interpolation method may not work well for news recommendation tasks for some reasons. First, it neglects the fine-grained personalization needs at both the temporal and spatial levels in news recommendation tasks. Second, due to the cold-user problem in real-world news recommendation tasks, the local personalized models may perform poorly, thus limiting the performance gain from model interpolation. To this end, we propose FINDING (Federated News Recommendation with Fine-grained Interpolation and Dynamic Clustering ), a novel personalized federated learning framework based on model interpolation. Specifically, we first propose the fine-grained model interpolation strategy which interpolates the local personalized models with the global model in a time-aware and layer-aware way. Then, to address the cold-user problem in news recommendation tasks, we adopt the group-level personalization approach where users are dynamically clustered into groups and the group-level personalized models are used for interpolation. Extensive experiments on two real-world datasets show that our method can effectively handle the above limitations of the current model interpolation method and alleviate the heterogeneity issue faced by traditional FL.
Explaining Learning to Rank Methods to Improve Them

State-of-the-art methods for Learning to Rank (LtR), either designed for tabular or textual data, are incredibly complex. Increasing the complexity of the models has many drawbacks, including difficulties in understanding the logic behind each prediction and a lack of trust in the system during its deployment. In this paper, which describes the author's goals during his Ph.D., there is an analysis and discussion of how we can use the ideas and tools coming from the eXplainable Artificial Intelligence (XAI) field to make the most effective methods for LtR understandable to the practitioners with the final goal of making them more efficient and/or understand better when they can be improved. The strategies adopted to achieve the aforementioned goals are different and based on the type of models analyzed, which go from more traditional LtR models based on ensembles of decision trees and using handcrafted features to fairly new neural LtR models using text data.
A Neuro-symbolic Approach to Enhance Interpretability of Graph Neural Network through the Integration of External Knowledge

Graph Neural Networks (GNNs) have shown remarkable performance in tackling complex tasks. However, interpreting the decision-making process of GNNs remains a challenge. To address the challenge, we explore representing the behaviour of a GNN in a representation space that is more transparent such as a knowledge graph, in a way that captures the behaviour of a GNN as a graph. Our initial experiments on the node classification task can represent the trained graph convolutional neural network (GCN) behaviour with some semantics uncovered by state-of-the-art approaches. This research offers a promising direction for enhancing GNN interpretability and understanding by providing structured, human-understandable representations and incorporating external knowledge for more accurate predictions.
CLosER: Conversational Legal Longformer with Expertise-Aware Passage Response Ranker for Long Contexts

In this paper, we investigate the task of response ranking in conversational legal search. We propose a novel method for conversational passage response retrieval (ConvPR) for long conversations in domains with mixed levels of expertise. Conversational legal search is challenging because the domain includes long, multi-participant dialogues with domain-specific language. Furthermore, as opposed to other domains, there typically is a large knowledge gap between the questioner (a layperson) and the responders (lawyers), participating in the same conversation. We collect and release a large-scale real-world dataset called LegalConv with nearly one million legal conversations from a legal community question answering (CQA) platform. We address the particular challenges of processing legal conversations, with our novel Conversational Legal Longformer with Expertise-Aware Response Ranker, called CLosER. The proposed method has two main innovations compared to state-of-the-art methods for ConvPR: (i) Expertise-Aware Post-Training; a learning objective that takes into account the knowledge gap difference between participants to the conversation; and (ii) a simple but effective strategy for re-ordering the context utterances in long conversations to overcome the limitations of the sparse attention mechanism of the Longformer architecture. Evaluation on LegalConv shows that our proposed method substantially and significantly outperforms existing state-of-the-art models on the response selection task. Our analysis indicates that our Expertise-Aware PostTraining, i.e., continued pre-training or domain/task adaptation, plays an important role in the achieved effectiveness. Our proposed method is generalizable to other tasks with domain-specific challenges and can facilitate future research on conversational search in other domains.
Diving into a Sea of Opinions: Multi-modal Abstractive Summarization with Comment Sensitivity

In the modern era, the rapid expansion of social media and the proliferation of the internet community has led to a multi-fold increase in the richness and range of views and outlooks expressed by readers and viewers. To obtain valuable insights from this vast sea of opinions, we present an inventive and holistic procedure for multi-modal abstractive summarization with comment sensitivity. Our proposed model utilizes both textual and visual modalities and examines the remarks provided by the readers to produce summaries that apprehend the significant points and opinions made by them. Our model features a transformer-based encoder that seamlessly processes both news articles and comments, merging them before transmitting the amalgamated information to the decoder. Additionally, the core segment of our architecture consists of an attention-based merging technique which is trained adversarially by means of a generator and discriminator to bridge the semantic gap between comments and articles. We have used a Bi-LSTM-based branch for image pointer generation. We assess our model on the reader-aware multi-document summarization (RA-MDS) dataset which contains news articles, their summaries, and related comments. We have extended the dataset by adding images pertaining to news articles in the corpus to increase the richness and diversity of the dataset. Our comprehensive experiments reveal that our model outperforms similar pre-trained models and baselines across two of the four evaluated metrics, showcasing its superior performance.
Lightweight Adaptation of Neural Language Models via Subspace Embedding

Traditional neural word embeddings are usually dependent on a richer diversity of vocabulary. However, the language models recline to cover major vocabularies via the word embedding parameters, in particular, for multilingual language models that generally cover a significant part of their overall learning parameters. In this work, we present a new compact embedding structure to reduce the memory footprint of the pre-trained language models with a sacrifice of up to 4% absolute accuracy. The embeddings vectors reconstruction follows a set of subspace embeddings and an assignment procedure via the contextual relationship among tokens from pre-trained language models. The subspace embedding structure1 calibrates to masked language models, to evaluate our compact embedding structure on similarity and textual entailment tasks, sentence and paraphrase tasks. Our experimental evaluation shows that the subspace embeddings achieve compression rates beyond 99.8% in comparison with the original embeddings for the language models on XNLI and GLUE benchmark suites.
Bi-channel Multiple Sparse Graph Attention Networks for Session-based Recommendation

Session-based Recommendation (SBR) has recently received significant attention due to its ability to provide personalized recommendations based on the interaction sequences of anonymous session users. The challenges facing SBR consist mainly of how to utilize information other than the current session and how to reduce the negative impact of irrelevant information in the session data on the prediction. To address these challenges, we propose a novel graph attention network-based model called Multiple Sparse Graph Attention Networks (MSGAT). MSGAT leverages two parallel channels to model intra-session and inter-session information. In the intra-session channel, we utilize a gated graph neural network to perform initial encoding, followed by a self-attention mechanism to generate the target representation. The global representation is then noise-reduced based on the target representation. Additionally, the target representation is used as a medium to connect the two channels. In the inter-session channel, the noise-reduced relation representation is generated using the global attention mechanism of target perception. Moreover, MSGAT fully considers session similarity from the intent perspective by integrating valid information from both channels. Finally, the intent neighbor collaboration module effectively combines relevant information to enhance the current session representation. Extensive experiments on five datasets demonstrate that simultaneous modeling of intra-session and inter-session data can effectively enhance the performance of the SBR model.
Age-Aware Guidance via Masking-Based Attention in Face Aging

Face age transformation aims to convert reference images into synthesized images so that they portray the specified target ages. The crux of this task is to change only age-related areas of the given image while maintaining the age-irrelevant areas unchanged. Nevertheless, a common limitation among most existing models is the struggle to generate high-quality aging images that effectively consider both crucial properties. To address this problem, we propose a novel GAN-based face-aging framework that utilizes age-aware Guidance via Masking-Based Attention (GMBA). Specifically, we devise an age-aware guidance module to adjust age-relevant and age-irrelevant attributes within the image seamlessly. By virtue of its capability, it enables the model to produce realistic age-transformed images that certainly preserve the input's identities while delicately imposing age-related properties. Experimental results show that our proposed GMBA outperformed other state-of-the-art methods in terms of identity preservation and accurate age conversion, as well as providing superior visual quality for age-transformed images.
Dual Interests-Aligned Graph Auto-Encoders for Cross-domain Recommendation in WeChat

Recently, cross-domain recommendation (CDR) has been widely studied in both research and industry since it can alleviate a long-standing challenge of traditional recommendation methods, i.e., data sparsity issue, by transferring the information from a relatively richer domain (termed source domain) to a sparser domain (termed target domain). To our best knowledge, most (if not all) existing CDR methods focus on transferring either the similar content information or the user preferences embedding from the source domain to the target domain. However, they fail to improve the recommendation performance in real-world recommendation scenarios where the items in the source domain are totally different from those in the target domain in terms of attributes. To solve the above issues, we analyzed the historical interactions of users from different domains in the WeChat platform, and found that if two users have similar interests (interactions) in one domain, they are very likely to have similar interests in another domain even though the items of these two domains are totally different in terms of attributes. Based on this observation, in this paper, we propose a novel model named Dual Interests-Aligned Graph Auto-Encoders (DIAGAE) by utilizing the inter-domain interest alignment of users. Besides, our proposed model DIAGAE also leverages graph decoding objectives to align intra-domain user interests, which makes the representation of two users who have similar interests in a single domain closer. Comprehensive experimental results demonstrate that our model DIAGAE outperforms state-of-the-art methods on both public benchmark datasets and online A/B tests in WeChat live-stream recommendation scenario. Our model DIAGAE now serves the major online traffic in WeChat live-streaming recommendation scenario.
Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognition

Incremental Named Entity Recognition (INER) involves the sequential learning of new entity types without accessing the training data of previously learned types. However, INER faces the challenge of catastrophic forgetting specific for incremental learning, further aggravated by background shift (i.e., old and future entity types are labeled as the non-entity type in the current task). To address these challenges, we propose a method called task Relation Distillation and Prototypical pseudo label (RDP) for INER. Specifically, to tackle catastrophic forgetting, we introduce a task relation distillation scheme that serves two purposes: 1) ensuring inter-task semantic consistency across different incremental learning tasks by minimizing inter-task relation distillation loss, and 2) enhancing the model's prediction confidence by minimizing intra-task self-entropy loss. Simultaneously, to mitigate background shift, we develop a prototypical pseudo label strategy that distinguishes old entity types from the current non-entity type using the old model. This strategy generates high-quality pseudo labels by measuring the distances between token embeddings and type-wise prototypes. We conducted extensive experiments on ten INER settings of three benchmark datasets (i.e., CoNLL2003, I2B2, and OntoNotes5). The results demonstrate that our method achieves significant improvements over the previous state-of-the-art methods, with an average increase of 6.08% in Micro F1 score and 7.71% in Macro F1 score.
System Initiative Prediction for Multi-turn Conversational Information Seeking

Identifying the right moment for a system to take the initiative is essential to conversational information seeking (CIS). Existing studies have extensively studied the clarification need prediction task, i.e., predicting when to ask a clarifying question, however, it only covers one specific system-initiative action. We define the system initiative prediction (SIP) task as predicting whether a CIS system should take the initiative at the next turn. Our analysis reveals that for effective modeling of SIP, it is crucial to capture dependencies between adjacent user?system initiative-taking decisions. We propose to model SIP by CRFs. Due to their graphical nature, CRFs are effective in capturing such dependencies and have greater transparency than more complex methods, e.g., LLMs. Applying CRFs to SIP comes with two challenges: (i) CRFs need to be given the unobservable system utterance at the next turn, and (ii) they do not explicitly model multi-turn features. We model SIP as an input-incomplete sequence labeling problem and propose a multi-turn system initiative predictor (MuSIc) that has (i) prior-posterior inter-utterance encoders to eliminate the need to be given the unobservable system utterance, and (ii) a multi-turn feature-aware CRF layer to incorporate multi-turn features into the dependencies between adjacent initiative-taking decisions. Experiments show that MuSIc outperforms LLM-based baselines including LLaMA, achieving state-of-the-art results on SIP. We also show the benefits of SIP on clarification need prediction and action prediction.
Tutorial on User Simulation for Evaluating Information Access Systems

With the emergence of various information access systems exhibiting increasing complexity, there is a critical need for sound and scalable means of automatic evaluation. To address this challenge, user simulation emerges as a promising solution. This half-day tutorial focuses on providing a thorough understanding of user simulation techniques designed specifically for evaluation purposes. We systematically review major research progress, covering both general frameworks for designing user simulators, and specific models and algorithms for simulating user interactions with search engines, recommender systems, and conversational assistants. We also highlight some important future research directions.
All-photonic one-way quantum repeaters with measurement-based error correction

Abstract Quantum repeater is the key technology enabler for long-distance quantum communication. To date, most of the existing quantum repeater protocols are designed based on specific quantum codes or graph states. In this paper, we propose a general framework for all-photonic one-way quantum repeaters based on the measurement-based error correction, which can be adapted to any Calderbank–Shor–Steane code including the recently discovered quantum low-density parity check (QLDPC) codes. We present a decoding scheme, where the error correction process is carried out at the destination based on the accumulated data from the measurements made across the network. This procedure not only outperforms the conventional protocols with independent repeaters but also simplifies the local quantum operations at repeaters. As an example, we numerically show that the [[48, 6, 8]] generalized bicycle code (as a small but efficient QLDPC code) has an equally good performance while reducing the resources by at least an order of magnitude.
A verifiable essential secret image sharing scheme based on HLRs (VESIS‐(<i>t</i>, <i>s</i>, <i>k</i>, <i>n</i>))

Abstract In traditional secret image sharing schemes, a secret image is shared among shareholders who have the same position. But if the shareholders have two different positions, essential and non‐essential, it is necessary to use essential secret image sharing schemes. In this article, a verifiable essential secret image sharing scheme based on HLRs is proposed. Shareholder's share consists of two parts. The first part is produced by the shareholders, which prevents the fraud of dealers. The second part is a shadow image that is produced by using HLRs and the first part of share. The verification of the first part of the shares is done for the first time by using multilinear and bilinear maps. Also, for verifying shadow images, Bloom Filters are used for the first time. The proposed scheme is more efficient than similar schemes, and for the first part of the shares, has formal security.
Deep Context Interest Network for Click-Through Rate Prediction

Click-Through Rate (CTR) prediction, estimating the probability of a user clicking on an item, is essential in industrial applications, such as online advertising. Many works focus on user behavior modeling to improve CTR prediction performance. However, most of those methods only model users' positive interests from users' click items while ignoring the context information, which is the display items around the clicks, resulting in inferior performance. In this paper, we highlight the importance of context information on user behavior modeling and propose a novel model named Deep Context Interest Network (DCIN), which integrally models the click and its display context to learn users' context-aware interests. DCIN consists of three key modules: 1) Position-aware Context Aggregation Module (PCAM), which performs aggregation of display items with an attention mechanism; 2) Feedback-Context Fusion Module (FCFM), which fuses the representation of clicks and display contexts through non-linear feature interaction; 3) Interest Matching Module (IMM), which activates interests related with the target item. Moreover, we provide our hands-on solution to implement DCIN on large-scale industrial systems. The significant improvements in both offline and online evaluations demonstrate the superiority of our proposed DCIN method. Notably, DCIN has been deployed on our online advertising system serving the main traffic, which brings 1.5% CTR and 1.5% RPM lift.
Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction

Conversion rate (CVR) prediction is an essential task for e-commerce platforms. However, refunds frequently occur after conversion in online shopping systems, which drives us to pay attention to effective conversion for building healthier services. This paper defines the probability of item purchasing without any subsequent refund as an effective conversion rate (ECVR). A simple paradigm for ECVR prediction is to decompose it into two sub-tasks: CVR prediction and post-conversion refund rate (RFR) prediction. However, RFR prediction suffers from data sparsity (DS) and sample selection bias (SSB) issues, as refund behaviors are only available after user purchase. Furthermore, there is delayed feedback in both sequentially dependent conversion and refund events, named cascade delayed feedback (CDF). Previous studies mainly focus on tackling DS and SSB or delayed feedback for a single event. To jointly tackle these issues in ECVR prediction, we propose an Entire space CAscade Delayed feedback modeling (ECAD) method. Specifically, ECAD deals with DS and SSB by constructing two tasks including CVR and conversion&refund rate (CVRFR) predictions using the entire space modeling framework. In addition, it carefully schedules auxiliary tasks to leverage both conversion and refund time within data to alleviate CDF. Experiments on the offline industrial dataset and online A/B testing demonstrate the effectiveness of ECAD. ECAD has been deployed in the Xianyu recommender system of Alibaba, contributing to a significant improvement of ECVR.
Improving Long-Tail Item Recommendation with Graph Augmentation

The ubiquitous long-tail distribution of inherent user behaviors results in worse recommendation performance for the items with fewer user records (i.e., tail items) than those with richer ones (i.e., head items). Graph-based recommendation methods (e.g., using graph neural networks) have recently emerged as a powerful tool for recommender systems, often outperforming traditional methods. However, existing techniques for alleviating the long-tail problem mainly focus on traditional methods. There is a lack of graph-based methods that can efficiently deal with the long-tail problem.