OpenRaiser/Intern-Atlas huggingface dataset:
Barter Exchange with Shared Item Valuations
Authors: Juan Luque, Sharmila Duppala, John P. Dickerson, Aravind Srinivasan
Venue: WWW
Year: 2024

In barter exchanges agents enter seeking to swap their items for other items on their wishlist. We consider a centralized barter exchange with a set of agents and items where each item has a positive value. The goal is to compute a (re)allocation of items maximizing the agents' collective utility subject to each agent's total received value being comparable to their total given value. Many such centralized barter exchanges exist and serve crucial roles; e.g., kidney exchange programs, which are often formulated as variants of directed cycle packing. We show finding a reallocation where each agent's total given and total received values are equal is NP-hard. On the other hand, we develop a randomized algorithm that achieves optimal utility in expectation and where, i) for any agent, with probability 1 their received value is at least their given value minus v^* where v^* is said agent's most valuable owned and wished-for item, and ii) each agent's given and received values are equal in expectation. Our algorithm builds on the dependent rounding techniques from \citetgandhiApproximationAlgorithmsPartial2004.
Are we Making Much Progress? Revisiting Chemical Reaction Yield Prediction from an Imbalanced Regression Perspective
Authors: Yihong Ma, Xiaobao Huang, Bozhao Nan, Nuno Moniz, Xiangliang Zhang, Olaf Wiest, Nitesh V. Chawla
Venue: WWW
Year: 2024

The yield of a chemical reaction quantifies the percentage of the target product formed in relation to the reactants consumed during the chemical reaction. Accurate yield prediction can guide chemists toward selecting high-yield reactions during synthesis planning, offering valuable insights before dedicating time and resources to wet lab experiments. While recent advancements in yield prediction have led to overall performance improvement across the entire yield range, an open challenge remains in enhancing predictions for high-yield reactions, which are of greater concern to chemists. In this paper, we argue that the performance gap in high-yield predictions results from the imbalanced distribution of real-world data skewed towards low-yield reactions, often due to unreacted starting materials and inherent ambiguities in the reaction processes. Despite this data imbalance, existing yield prediction methods continue to treat different yield ranges equally, assuming a balanced training distribution. Through extensive experiments on three real-world yield prediction datasets, we emphasize the urgent need to reframe reaction yield prediction as an imbalanced regression problem. Finally, we demonstrate that incorporating simple cost-sensitive re-weighting methods can significantly enhance the performance of yield prediction models on underrepresented high-yield regions.
Temporal Conformity-aware Hawkes Graph Network for Recommendations
Authors: Chenglong Ma, Yongli Ren, Pablo Castells, Mark Sanderson
Venue: WWW
Year: 2024

Many existing recommender systems (RSs) assume user behavior is governed solely by their interests. However, the peer effect often influences individual decision-making, which leads to conformity behavior. Conventional solutions that eliminate indiscriminately such bias may cause RSs to neglect valuable information and depersonalize the recommendation results. Also, conformity can transform into user interest, e.g., discovering new tastes after a glance at popular music. By better representing different forms of conformity influence, we can do a better job at interest mining and debiasing. In certain extreme circumstances, the herd effect may be exacerbated by user anxiety with uncertainty (e.g., panic buying during the COVID-19 pandemic). RSs may thus fail to respond in time due to sudden and dramatic changes. Moreover, many existing studies potentially conflate conformity bias with popularity bias and lump together various factors responsible for differences in popularity. In this paper, we identify two distinct types of conformity behavior: informational conformity and normative conformity. To address this, we introduce the TCHN model, which utilizes attentional Hawkes processes to disentangle user self-interest and conformity in a personalized manner. Our approach incorporates temporal graph attention networks to capture users' stable and volatile dynamics. We conduct experiments on three real-world datasets, which uncover diverse levels of conformity among users. The results show that TCHN excels in recommendation accuracy, diversity, and fairness across various user groups.
Finding What Users Look for by Attribute-Aware Personalized Item Comparison in Relevant Recommendation
Authors: Rui Ma, Dike Sun, Jincheng Xu, Jingsong Yuan, Jiandong Zhang
Venue: WWW
Year: 2024

Relevant recommendation is a distinctive recommendation scenario in e-commerce platforms, which provides an extended set of items that are relevant to the trigger item (the item that triggers the relevant recommendation). Different from the general recommendations whose item feeds are diversified, relevant recommendation regards the trigger item as a key component. From one perspective, the trigger item reveals users' current interests and determines the range of the recommendation results. From the other perspective, users may have the mindset to look for items that have directional attribute differences from the trigger item. In this paper, we present an attribute-aware personalized item comparison framework. Under this framework, an item subtraction module is first applied over the trigger item and the candidate item, which calculates their directional difference with consideration of their intrinsic similarity. Then two modules are used to estimate users' preference for this current item pair: one learns the collective preference of all users, and the other learns the current user's personal evolutional preference. Experiments on a CTR prediction task over both a public dataset and an industrial dataset from our shopping app show that the proposed method outperforms the state-of-the-art algorithms and also achieves better generalization ability.
A Fast Similarity Matrix Calibration Method with Incomplete Query
Authors: Changyi Ma, Runsheng Yu, Youzhi Zhang
Venue: WWW
Year: 2024

The similarity matrix is at the core of similarity search problems. However, incomplete observations are ubiquitous in real scenarios leading to a less accurate similarity matrix. To alleviate this problem, in this paper, based on the key insight that the similarity matrix enjoys both the symmetric and positive semi-definiteness (PSD) properties, we propose a novel similarity matrix calibration method, which is scalable, effective, and sound. Specifically, we establish the PSD property as a constraint for the similarity matrix calibration problem and propose a novel similarity matrix calibration method to estimate the similarity matrix, which approximates the unknown complete ground-truth similarity matrix. To enable a fast optimization process, we further develop a general approximated algorithm that bypasses the computation of singular values. Theoretical analysis ensures stable calibration performance and convergence speed. Extensive experiments of similarity matrix calibration on real-world datasets demonstrate that our proposed method outperforms baseline methods in terms of both accuracy and speed.
Multimodal Conditioned Diffusion Model for Recommendation
Authors: Haokai Ma, Yimeng Yang, Lei Meng, Ruobing Xie, Xiangxu Meng
Venue: WWW
Year: 2024

Multimodal recommendation aims at to modeling the feature distributions of items by using their multi-modal information. Prior efforts typically focus on the denoising of the user-item graph with a degree-sensitive strategy, which may not well-handle the users' consistent preference across modalities. More importantly, it has been observed that existing methods may learn ill-posed item embeddings due to their focus on a specific auxiliary optimization task for multimodal representations rather than explicitly modeling them. This paper therefore presents a solution that takes the advantages of the explicit uncertainty injection ability of Diffusion Model (DM) for the modeling and fusion of multi-modal information. Specifically, we propose a novel Multimodal Conditioned Diffusion Model for Recommendation (MCDRec), which tailors DM with two technical modules to model the high-order multimodal knowledge. The first module is multimodal-conditioned representation diffusion (MRD), which integrates pre-extracted multimodal knowledge into the item representation modeling via a tailored DM. This smoothly bridges the insurmountable gap between the multi-modal content features and the collaborative signals. Secondly, with the diffusion-guided graph denoising (DGD) module, MCDRec may effectively denoise the user-item graph by filtering the occasional interactions in user historical behaviors. This is achieved with the power of DM in aligning the users' collaborative preferences with their shared items' content information. Extensive experiments compared to several SOTA baselines on two real-word datasets demonstrate the effectiveness of MCDRec. The specific visualization also reveals the potential of MRD to precisely handling the high-order representation correlations among the user embeddings and the multi-modal heterogeneous representations of items.
TransDrift: Modeling Word-Embedding Drift using Transformer
Authors: Nishtha Madaan, Prateek Chaudhury, Nishant Kumar, Srikanta Bedathur
Venue: WWW
Year: 2024

In modern NLP applications, word embeddings are a crucial backbone that can be readily shared across a number of tasks. However, as the text distributions change and word semantics evolve over time, the downstream applications using the embeddings can suffer if the word representations do not conform to the data drift. Thus, maintaining word embeddings to be consistent with the underlying data distribution is a key problem. In this work, we tackle this problem and propose TransDrift\footnoteCodebase: https://github.com/data-iitd/transdrift , a transformer-based prediction model for word embeddings. Leveraging the flexibility of transformer, our model accurately learns the dynamics of the embedding drift and predicts the future embedding. In experiments, we compare with existing methods and show that our model makes significantly more accurate predictions of the word embedding than the baselines. Crucially, by applying the predicted embeddings as a backbone for downstream classification tasks, we show that our embeddings lead to superior performance compared to the previous methods.
An Open Platform for Quality Measures in a Linked Data Index
Authors: Pierre Maillot, Jennie Andersen, Sylvie Cazalens, Catherine Faron, Fabien Gandon, Philippe Lamarre, Franck Michel
Venue: WWW
Year: 2024

There is a great diversity of RDF datasets publicly available on the web. Choosing among them requires assessing their "fitness for use'' for a particular use case, and thus, finding the right quality measures and evaluating data sources according to them. However, this is not an easy task due to the large number of possible quality measures, and the multiplicity of implementation and assessment platforms. Therefore, there is a need for a common way to define measures and evaluate RDF datasets, using open standards and tools. IndeGx is a SPARQL-based framework to design indexes of Knowledge Graphs declaratively. We extend it to support more advanced data quality measures. We demonstrate our approach by reproducing two existing measures, showing how one can formalize and add measures using such an open declarative framework.
The 1st International Workshop on Graph Foundation Models (GFM)
Authors: Haitao Mao, Jianan Zhao, Xiaoxin He, Zhikai Chen, Qian Huang, Zhaocheng Zhu, Jian Tang, Michael M. Bronstein, Xavier Bresson, Bryan Hooi, Haiyang Zhang, Xianfeng Tang, Luo Chen, Jiliang Tang
Venue: WWW
Year: 2024

Foundation models such as GPT-4 for natural language processing (NLP), Flamingo for computer vision (CV), have set new benchmarks in AI by delivering state-of-the-art results across various tasks with minimal task-specific data. Despite their success, the application of these models to the graph domain is challenging due to the relational nature of graph-structured data. To address this gap, we propose the Graph Foundation Model (GFM) Workshop, the first workshop for GFMs, dedicated to exploring the adaptation and development of foundation models specifically designed for graph data. The GFM workshop focuses on two critical questions: (1) How can the underlying capabilities of existing foundation models be effectively applied to graph data? (2) What foundational principles should guide the creation of models tailored to the graph domain? Through a curated set of panel sections, keynote talks, and paper presentations, our workshop intends to catalyze innovative approaches and theoretical frameworks for Graph Foundation Models (GFMs). We target a broad audience, encompassing researchers, practitioners, and students, and aim to lay the groundwork for the next wave of breakthroughs in integrating graph data with foundation models.
How Good are LLMs in Generating Personalized Advertisements?
Authors: Elyas Meguellati, Lei Han, Abraham Bernstein, Shazia Sadiq, Gianluca Demartini
Venue: WWW
Year: 2024

In this paper, we explore the potential of large language models (LLMs) in generating personalized online advertisements (ads) tailored to specific personality traits, focusing on openness and neuroticism. We conducted a user study involving two tasks to understand the performance of LLM-generated ads compared to human-written ads in different online environments. Task 1 simulates a social media environment where users encounter ads while scrolling through their feed. Task 2 mimics a shopping website environment where users are presented with multiple sponsored products side-by-side. Our results indicate that LLM-generated ads targeting the openness trait positively impact user engagement and preferences, with performance comparable to human-written ads. Furthermore, in both scenarios, the overall effectiveness of LLM-generated ads was found to be similar to that of human-written ads, highlighting the potential of LLM-generated personalised content to rival traditional advertising methods with the added advantage of scalability. This study underscores the need for cautious consideration in the deployment of LLM-generated content at scale. While our findings confirm the scalability and potential effectiveness of LLM-generated content, there is an equally pressing concern about the ease with which it can be misused.
In The Beginning, Let There Be The Word: Challenges and Insights in Applying Sentiment Analysis to Social Research
Authors: Andrzej Meler
Venue: WWW
Year: 2024

Sentiment analysis based on lexical corpora is widely employed despite its inherent limitations in capturing nuances such as sarcasm and irony. This research delves into the application of sentiment analysis to political communication. To address the limitations of the Bag of Words methodology, a comparative study of sentiment analysis tools and emotion detection from speech is conducted, using automated speech recognition as a benchmark. Emotion recognition from speech has shown promising results, indicating its potential superiority over other methods [1], [2]. This study uses media material from Polish radio and television broadcasts, focusing on political interviews during a significant period marked by a high-profile assassination attempt. Results indicate challenges at the micro-level, but aggregated data reveals a significant correlation between valence measured from voice and text. While sentiment analysis may lack sensitivity in capturing mourning-related discourse, it proves effective in political communication devoid of such nuances. This suggests that valence in sentiment analysis reflects emotional content derived from intonation fairly accurately.
Statistical Confidence in Mining Power Estimates for PoW Blockchains
Authors: Mary Milad, Christina Ovezik, Dimitris Karakostas, Daniel W. Woods
Venue: WWW
Year: 2024

The security of blockchain systems depends on the distribution of mining power across participants. If sufficient mining power is controlled by one entity, they can force their own version of events. This may allow them to double spend coins, for example. For Proof of Work (PoW) blockchains, however, the distribution of mining power cannot be read directly from the blockchain and must instead be inferred from the number of blocks mined in a specific sample window. We introduce a framework to quantify this statistical uncertainty for the Nakamoto coefficient, which is a commonly-used measure of blockchain decentralization. We show that aggregating blocks over a day can lead to considerable uncertainty, with Bitcoin failing more than half the hypothesis tests (α=0.05) when using a daily granularity. For these reasons, we recommend that blocks are aggregated over a sample window of at least 7 days. Instead of reporting a single value, our approach produces a range of possible Nakamoto coefficient values that have statistical support at a particular significance level α.
Tackling Long-Tail Entities for Temporal Knowledge Graph Completion
Authors: Mehrnoosh Mirtaheri, Ryan A. Rossi, Sungchul Kim, Kanak Mahadik, Tong Yu, Xiang Chen, Mohammad Rostami
Venue: WWW
Year: 2024

Most Temporal Knowledge Graphs (TKGs) exhibit a long-tail entity distribution, where the majority of entities have sparse connections. Existing TKG completion methods struggle with managing new or unseen entities that often lack sufficient connections. In this paper, we introduce a model-agnostic enhancement layer that can be integrated with any existing TKG completion method to improve its performance. This enhancement layer employs a broader, global definition of entity similarity, transcending the limitations of local neighborhood proximity found in Graph Neural Network (GNN) based methods. Additionally, we conduct our evaluations in a novel, realistic setup that treats the TKG as a stream of evolving data. Evaluations on two benchmark datasets demonstrate that our framework surpasses existing methods in overall link prediction, inductive link prediction, and in addressing long-tail entities. Notably, our approach achieves a 10% improvement in MRR on one dataset and a 15% increase on another.
LLM-Guided Counterfactual Data Generation for Fairer AI
Authors: Ashish Mishra, Gyanaranjan Nayak, Suparna Bhattacharya, Tarun Kumar, Arpit Shah, Martin Foltin
Venue: WWW
Year: 2024

With the widespread adoption of deep learning-based models in practical applications, concerns about their fairness have become increasingly prominent. Existing research indicates that both the model itself and the datasets on which they are trained can contribute to unfair decisions. In this paper, we address the data-related aspect of the problem, aiming to enhance the data to guide the model towards greater trustworthiness. Due to their uncontrolled curation and limited understanding of fairness drivers, real-world datasets pose challenges in eliminating unfairness. Recent findings highlight the potential of Foundation Models in generating substantial datasets. We leverage these foundation models in conjunction with state-of-the-art explainability and fairness platforms to generate counterfactual examples. These examples are used to augment the existing dataset, resulting in a more fair learning model. Our experiments were conducted on the CelebA and UTKface datasets, where we assessed the quality of generated counterfactual data using various bias-related metrics. We observed improvements in bias mitigation across several protected attributes in the fine-tuned model when utilizing counterfactual data.
NETEVOLVE: Social Network Forecasting using Multi-Agent Reinforcement Learning with Interpretable Features
Authors: Kentaro Miyake, Hiroyoshi Ito, Christos Faloutsos, Hirotomo Matsumoto, Atsuyuki Morishima
Venue: WWW
Year: 2024

Predicting how social networks change in the future is important in many applications. Results in social network research have shown that the change in the network can be explained by a small number of concepts, such as "homophily" and "transitivity". However, existing prediction methods require many latent features that are not connected to such concepts, making the methods' black boxes and their prediction results difficult to interpret, making them harder to derive scientific knowledge about social networks. In this study, we propose NetEvolve a novel multi-agent reinforcement learning-based method that predicts changes in a given social network. Given a sequence of changes as training data, NetEvolve learns the characteristics of the nodes with interpretable features, such as how the node feels rewards for connecting with similar people and the cost of the connection itself. Based on the learned feature, NetEvolve makes a forecast based on multi-agent simulation. The method achieves comparable or better accuracy than existing methods in predicting network changes in real-world social networks while keeping the prediction results interpretable.
Graph Principal Flow Network for Conditional Graph Generation
Authors: Zhanfeng Mo, Tianze Luo, Sinno Jialin Pan
Venue: WWW
Year: 2024

Conditional graph generation is crucial and challenging since the conditional distribution of graph topology and feature is complicated and the semantic information is hard to capture by the generative model. In this work, we propose a novel graph conditional generative model, Graph Principal Flow Network (GPrinFlowNet), which enables us to progressively generate high-quality graphs from low- to high-frequency components for a given graph label. We show that GPrinFlowNet follows a coarse-to-fine resolution generation curriculum, which enables it to capture subtle semantic information by generating intermediate graphs with high mutual information relative to the graph label. Extensive experiments and ablation studies showcase that our model achieves state-of-the-art performance compared to existing conditional graph generation models.
Efficacy of Large Language Models in Predicting Hindi Movies' Attributes: A Comprehensive Survey and Content-Based Analysis
Authors: Prabir Mondal, Siddharth Singh, Kushum, Sriparna Saha, Jyoti Prakash Singh, Brijraj Singh, Niranjan Pedanekar
Venue: WWW
Year: 2024

This research explores the efficacy of four state-of-the-art Large Language Models (LLMs): GPT-3.5-turbo-0301, Vicuna, PaLM 2, and Dolly in predicting (i) movie genres using audio transcripts of movie trailers and (ii) meta-information such as director and cast details using movie name and its year-of-release (YoR) for Hindi movies. In the contemporary landscape, training models for movie meta-information prediction often demand extensive data and parameters, posing significant challenges. We aim to discern whether LLMs mitigate these challenges. Focusing on Hindi movies within the Flickscore dataset, our study concentrates on trailer data. Preliminary findings reveal that GPT-3.5 stands out as the most effective LLM in predicting movie meta-information. Despite the inherent complexities of predicting diverse aspects such as genres and user preferences, GPT-3.5 exhibits promising capabilities. This research not only contributes to advancing our understanding of LLMs in the context of movie-related tasks but also sheds light on their potential application in Recommendation Systems (RS), indicating a notable leap forward in user preference comprehension and personalized content recommendations.