OpenRaiser/Intern-Atlas huggingface dataset:
Effective influence maximization with priority
Authors: Jinghao Wang, Yanping Wu, Xiaoyang Wang, Chen Chen, Ying Zhang, Lu Qin
Venue: Conference WWW 2025
Year: 2025

Influence maximization (IM) aims to identify a small set of influential users to maximize the information spread. It has been widely applied in the context of viral marketing, where a company distributes incentives to a few influencers to promote the product. However, in practical scenarios, not all users hold equal importance and certain users need to be prioritized for the specific requirements. Motivated by this, recently, a variant problem of IM, called influence maximization with priority (IMP), has been proposed. Given a graph G=(V,E), a priority set P ⊆ V and a threshold T ∈ [0, |P|], IMP aims to identify a set of k nodes (termed seeds ) to maximize the expected number of activated nodes in G while satisfying that the expected number of activated nodes in~P is no less than the given threshold. Nevertheless, we show that existing solutions for IMP are inferior in maximizing the influence spread in G, and can only offer poor approximation ratios in many cases. To address these limitations, in this paper, we first propose a novel framework named SAR with both superior effectiveness and strong theoretical guarantees. In addition, to obtain more practical results, we study the IMP problem under the adaptive setting, where the seeds are iteratively selected after observing the diffusion result of the previous seeds. We design an effective method AAS that achieves expected approximation guarantees. Extensive experiments demonstrate that, compared with the state-of-the-art method, SAR achieves up to 22.3% larger spread and ås achieves up to 42.6% larger spread, with both exhibiting a higher approximation ratio.
How much do prompting methods help llms on quantitative reasoning with irrelevant information?
Authors: Seok Hwan Song, Wallapak Tavanapong
Venue: Conference CIKM 2024
Year: 2024

Real-world quantitative reasoning problems are complex, often including extra information irrelevant to the question (or "IR noise" for short). State-of-the-art (SOTA) prompting methods have increased the Large Language Model's ability for quantitative reasoning on grade-school Math Word Problems (MWPs). To assess how well these SOTA methods handle IR noise, we constructed four new datasets with IR noise, each consisting of 300 problems from each of the four public datasets: MAWPS, ASDiv, SVAMP, and GSM8K, with added IR noise. We called the collection of these new datasets "MPN"--Math Word Problems with IR Noise. We evaluated SOTA prompting methods using MPN. We propose Noise Reduction Prompting (NRP) and its variant (NRP+) to reduce the impact of IR noise. Findings: Our IR noise significantly degrades the performance of Chain-of-Thought (CoT) Prompting on three different backend models: ChatGPT (gpt-3.5-turbo-0613), PaLM2, and Llama3-8B-instruct. Among them, ChatGPT offers the best accuracy on MPN with and without IR noise. With IR noise, performances of CoT, Least-To-Most Prompting, Progressive-Hint Prompting, and Program-aided Language Models with ChatGPT were significantly impacted, each with an average accuracy drop of above 12%. NRP is least impacted by the noise, with a drop in average accuracy to only around 1.9%. Our NRP+ and NRP perform comparably in the presence of IR noise.
Fairness of extractive text summarization
Authors: Anurag Shandilya, Kripabandhu Ghosh, Saptarshi Ghosh
Venue: Conference WWW 2018
Year: 2018

We propose to evaluate extractive summarization algorithms from a completely new perspective. Considering that an extractive summarization algorithm selects a subset of the textual units in the input data for inclusion in the summary, we investigate whether this selection is fair. We use several summarization algorithms over datasets that have a sensitive attribute (e.g., gender, political leaning) associated with the textual units, and find that the generated summaries often have very different distributions of the said attribute. Specifically, some classes of the textual units are under-represented in the summaries according to the fairness notion of adverse impact. To our knowledge, this is the first work on fairness of summarization, and is likely to open up interesting research problems.
Cross domain recommendation via bi-directional transfer graph collaborative filtering networks
Authors: Meng Liu, Jianjun Li, Guohui Li, Peng Pan
Venue: Conference CIKM 2020
Year: 2020

Data sparsity is a challenge problem that most modern recommender systems are confronted with. By leveraging the knowledge from relevant domains, the cross-domain recommendation technique can be an effective way of alleviating the data sparsity problem. In this paper, we propose a novel Bi-directional Transfer learning method for cross-domain recommendation by using Graph Collaborative Filtering network as the base model (BiTGCF). BiTGCF not only exploits the high-order connectivity in user-item graph of single domain through a novel feature propagation layer, but also realizes the two-way transfer of knowledge across two domains by using the common user as the bridge. Moreover, distinct from previous cross-domain collaborative filtering methods, BiTGCF fuses users' common features and domain-specific features during transfer. Experimental results on four couple benchmark datasets verify the effectiveness of BiTGCF over state-of-the-art models in terms of bi-directional cross domain recommendation.
Could small language models serve as recommenders? towards data-centric cold-start recommendation
Authors: Xuansheng Wu, Huachi Zhou, Yucheng Shi, Wenlin Yao, Xiao Huang, Ninghao Liu
Venue: Conference WWW 2024
Year: 2024

Recommendation systems help users find matched items based on their previous behaviors. Personalized recommendation becomes challenging in the absence of historical user-item interactions, a practical problem for startups known as the system cold-start recommendation. While existing research addresses cold-start issues for either users or items, we still lack solutions for system cold-start scenarios. To tackle the problem, we propose PromptRec, a simple but effective approach based on in-context learning of language models, where we transform the recommendation task into the sentiment analysis task on natural language containing user and item profiles. However, this naive approach heavily relies on the strong in-context learning ability emerged from large language models, which could suffer from significant latency for online recommendations. To solve the challenge, we propose to enhance small language models for recommender systems with a data-centric pipeline, which consists of: (1) constructing a refined corpus for model pre-training; (2) constructing a decomposed prompt template via prompt pre-training. They correspond to the development of training data and inference data, respectively. The pipeline is supported by a theoretical framework that formalizes the connection between in-context recommendation and language modeling. To evaluate our approach, we introduce a cold-start recommendation benchmark, and the results demonstrate that the enhanced small language models can achieve comparable cold-start recommendation performance to that of large models with only 17% of the inference time. To the best of our knowledge, this is the first study to tackle the system cold-start recommendation problem. We believe our findings will provide valuable insights for future works. The benchmark and implementations are available at https://github.com/JacksonWuxs/PromptRec.
Geometric disentangled collaborative filtering
Authors: Yiding Zhang, Chaozhuo Li, Xing Xie, Xiao Wang, Chuan Shi, Yuming Liu, Hao Sun, Liangjie Zhang, Weiwei Deng, Qi Zhang
Venue: Conference SIGIR 2022
Year: 2022

Learning informative representations of users and items from the historical interactions is crucial to collaborative filtering (CF). Existing CF approaches usually model interactions solely within the Euclidean space. However, the sophisticated user-item interactions inherently present highly non-Euclidean anatomy with various types of geometric patterns (i.e., tree-likeness and cyclic structures). The Euclidean-based models may be inadequate to fully uncover the intent factors beneath such hybrid-geometry interactions. To remedy this deficiency, in this paper, we study the novel problem of Geometric Disentangled Collaborative Filtering (GDCF), which aims to reveal and disentangle the latent intent factors across multiple geometric spaces. A novel generative GDCF model is proposed to learn geometric disentangled representations by inferring the high-level concepts associated with user intentions and various geometries. Empirically, our proposal is extensively evaluated over five real-world datasets, and the experimental results demonstrate the superiority of GDCF.
Multimodal relation extraction via a mixture of hierarchical visual context learners
Authors: Xiyang Liu, Chunming Hu, Richong Zhang, Kai Sun, Samuel Mensah, Yongyi Mao
Venue: Conference WWW 2024
Year: 2024

Multimodal relation extraction is a fundamental task of multimodal information extraction. Recent studies have shown promising results by integrating hierarchical visual features from local regions, like image patches, to the broader global regions that form the entire image. However, research to date has largely ignored the understanding of how hierarchical visual semantics are represented and the characteristics that can benefit relation extraction. To bridge this gap, we propose a novel two-stage hierarchical visual context fusion transformer incorporating the mixture of multimodal experts framework to effectively represent and integrate hierarchical visual features into textual semantic representations. In addition, we introduce the concept of hierarchical tracking maps to facilitate the understanding of the intrinsic mechanisms of image information processing involved in multimodal models. We thoroughly investigate the implications of hierarchical visual contexts through four dimensions: performance evaluation, the nature of auxiliary visual information, the patterns observed in the image encoding hierarchy, and the significance of various visual encoding levels. Empirical studies show that our approach achieves new state-of-the-art performance on the MNRE dataset.
Fake resume attacks: Data poisoning on online job platforms
Authors: Michiharu Yamashita, Thanh Tran, Dongwon Lee
Venue: Conference WWW 2024
Year: 2024

While recent studies have exposed various vulnerabilities incurred from data poisoning attacks in many web services, little is known about the vulnerability on online professional job platforms (e.g., LinkedIn and Indeed). In this work, first time, we demonstrate the critical vulnerabilities found in the common Human Resources (HR) task of matching job seekers and companies on online job platforms. Capitalizing on the unrestricted format and contents of job seekers' resumes and easy creation of accounts on job platforms, we demonstrate three attack scenarios: (1) company promotion attack to increase the likelihood of target companies being recommended, (2) company demotion attack to decrease the likelihood of target companies being recommended, and (3) user promotion attack to increase the likelihood of certain users being matched to certain companies. To this end, we develop an end-to-end "fake resume" generation framework, titled FRANCIS, that induces systematic prediction errors via data poisoning. Our empirical evaluation on real-world datasets reveals that data poisoning attacks can markedly skew the results of matchmaking between job seekers and companies, regardless of underlying models, with vulnerability amplified in proportion to poisoning intensity. These findings suggest that the outputs of various services from job platforms can be potentially hacked by malicious users.
Broadening the view: Demonstration-augmented prompt learning for conversational recommendation
Authors: Huy Dao, Yang Deng, Dung D. Le, Lizi Liao
Venue: Conference SIGIR 2024
Year: 2024

Conversational Recommender Systems (CRSs) leverage natural language dialogues to provide tailored recommendations. Traditional methods in this field primarily focus on extracting user preferences from isolated dialogues. It often yields responses with a limited perspective, confined to the scope of individual conversations. Recognizing the potential in collective dialogue examples, our research proposes an expanded approach for CRS models, utilizing selective analogues from dialogue histories and responses to enrich both generation and recommendation processes. This introduces significant research challenges, including: (1) How to secure high-quality collections of recommendation dialogue exemplars? (2) How to effectively leverage these exemplars to enhance CRS models? To tackle these challenges, we introduce a novel Demonstration-enhanced Conversational Recommender System (DCRS), which aims to strengthen its understanding on the given dialogue contexts by retrieving and learning from demonstrations. In particular, we first propose a knowledge-aware contrastive learning method that adeptly taps into the mentioned entities and the dialogue's contextual essence for pretraining the demonstration retriever. Subsequently, we further develop two adaptive demonstration-augmented prompt learning approaches, involving contextualized prompt learning and knowledge-enriched prompt learning, to bridge the gap between the retrieved demonstrations and the two end tasks of CRS, i.e., response generation and item recommendation, respectively. Rigorous evaluations on two established benchmark datasets underscore DCRS's superior performance over existing CRS methods in both item recommendation and response generation.
A contrastive framework with user, item and review alignment for recommendation
Authors: Hoang V. Dong, Yuan Fang, Hady W. Lauw
Venue: Conference WSDM 2025
Year: 2025

Learning effective latent representations for users and items is the cornerstone of recommender systems. Traditional approaches rely on user-item interaction data to map users and items into a shared latent space, but the limiting factor is often data sparsity. While reviews could mitigate this sparsity, existing review-aware recommendation models may not have fully exploited their potential. First, they typically rely heavily on reviews as side information or additional features, but reviews are not universal, with many users and items lacking them. Second, such approaches use reviews as supplementary information, allowing potential divergence or inconsistency between the review representations and the user-item space. To overcome these limitations, our work introduces a Review-centric Contrastive Alignment Framework for Recommendation (ReCAFR), which incorporates reviews into the core learning process, ensuring alignment among user, item, and review representations within a unified space. Specifically, we leverage two self-supervised contrastive strategies that not only exploit the review augmentation to alleviate sparsity, but also align the tripartite representations to enhance robustness. Empirical studies on public benchmark datasets demonstrate the effectiveness and robustness of ReCAFR
Llm-powered multi-agent framework for goal-oriented learning in intelligent tutoring system
Authors: Tianfu Wang, Yi Zhan, Jianxun Lian, Zhengyu Hu, Nicholas Jing Yuan, Qi Zhang, Xing Xie, Hui Xiong
Venue: Conference WWW 2025
Year: 2025

Intelligent Tutoring Systems (ITSs) have revolutionized education by offering personalized learning experiences. However, as goal-oriented learning, which emphasizes efficiently achieving specific objectives, becomes increasingly important in professional contexts, existing ITSs often struggle to deliver this type of targeted learning experience. In this paper, we propose GenMentor, an LLM-powered multi-agent framework designed to deliver goal-oriented, personalized learning within ITS. GenMentor begins by accurately mapping learners' goals to required skills using a fine-tuned LLM trained on a custom goal-to-skill dataset. After identifying the skill gap, it schedules an efficient learning path using an evolving optimization approach, driven by a comprehensive and dynamic profile of learners' multifaceted status. Additionally, GenMentor tailors learning content with an exploration-drafting-integration mechanism to align with individual learner needs. Extensive automated and human evaluations demonstrate GenMentor's effectiveness in learning guidance and content quality. Furthermore, we have deployed it in practice and also implemented it as an application. Practical human study with professional learners further highlights its effectiveness in goal alignment and resource targeting, leading to enhanced personalization. Supplementary resources are available at https://github.com/GeminiLight/gen-mentor.
Hsf: Defending against jailbreak attacks with hidden state filtering
Authors: Cheng Qian, Hainan Zhang, Lei Sha, Zhiming Zheng
Venue: Conference WWW 2025
Year: 2025

With the growing deployment of LLMs in daily applications like chatbots and content generation, efforts to ensure outputs align with human values and avoid harmful content have intensified. However, increasingly sophisticated jailbreak attacks threaten this alignment, aiming to induce unsafe outputs. Current defense efforts either focus on prompt rewriting or detection, which are limited in effectiveness due to the various design of jailbreak prompts, or on output control and detection, which are computationally expensive as they require LLM inference. Therefore, designing a pre-inference defense method that resists diverse jailbreak prompts is crucial for preventing LLM jailbreak attacks. We observe that jailbreak attacks, safe queries, and harmful queries exhibit different clustering patterns within the LLM's hidden state representation space. This suggests that by leveraging the LLM's hidden state representational capabilities, we can analyze the LLM's forthcoming behavior and proactively intervene for defense. In this paper, we propose a jailbreak attack defense strategy based on a Hidden State Filter (HSF), a lossless architectural defense mechanism that enables the model to preemptively identify and reject adversarial inputs before the inference process begins. We activate its defensive potential through an additional plugin module, effectively framing the defense task as a classification problem. Experimental results on two benchmark datasets, utilizing three different LLMs, show that HSF significantly enhances resilience against six cutting-edge jailbreak attacks. It significantly reduces the success rate of jailbreak attacks while minimally impacting responses to benign user queries, with negligible inference overhead, and outperforming defense baselines.Our code and data are available at https://anonymous.4open.science/r/Hidden-State-Filtering-8652/
A multi-domain benchmark for personalized search evaluation
Authors: Elias Bassani, Pranav Kasela, Alessandro Raganato, Gabriella Pasi
Venue: Conference CIKM 2022
Year: 2022

Personalization in Information Retrieval has been a hot topic in both academia and industry for the past two decades. However, there is still a lack of high-quality standard benchmark datasets for conducting offline comparative evaluations in this context. To mitigate this problem, in the past few years, approaches to derive synthetic datasets suited for evaluating Personalized Search models have been proposed. In this paper, we put forward a novel evaluation benchmark for Personalized Search with more than 18 million documents and 1.9 million queries across four domains. We present a detailed description of the benchmark construction procedure, highlighting its characteristics and challenges. We provide baseline performance including pre-trained neural models, opening room for the evaluation of personalized approaches, as well as domain adaptation and transfer learning scenarios. We make both datasets and models available for future research.
Resources for brewing beir: Reproducible reference models and statistical analyses
Authors: Ehsan Kamalloo, Nandan Thakur, Carlos Lassance, Xueguang Ma, Jheng-Hong Yang, Jimmy Lin
Venue: Conference SIGIR 2024
Year: 2024

BEIR is a benchmark dataset originally designed for zero-shot evaluation of retrieval models across 18 different domain/task combinations. In recent years, we have witnessed the growing popularity of models based on representation learning, which naturally begs the question: How effective are these models when presented with queries and documents that differ from the training data? While BEIR was designed to answer this question, our work addresses two shortcomings that prevent the benchmark from achieving its full potential: First, the sophistication of modern neural methods and the complexity of current software infrastructure create barriers to entry for newcomers. To this end, we provide reproducible reference implementations that cover learned dense and sparse models. Second, comparisons on BEIR are performed by reducing scores from heterogeneous datasets into a single average that is difficult to interpret. To remedy this, we present meta-analyses focusing on effect sizes across datasets that are able to accurately quantify model differences. By addressing both shortcomings, our work facilitates future explorations in a range of interesting research questions.
SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval
Authors: Liang Pang 0001, Jun Xu 0001, Qingyao Ai, Yanyan Lan, Xueqi Cheng, Jirong Wen
Venue: Conference SIGIR 2020
Year: 2020

In learning-to-rank for information retrieval, a ranking model is automatically learned from the data and then utilized to rank the sets of retrieved documents. Therefore, an ideal ranking model would be a mapping from a document set to a permutation on the set, and should satisfy two critical requirements: (1)~it should have the ability to model cross-document interactions so as to capture local context information in a query; (2)~it should be permutation-invariant, which means that any permutation of the inputted documents would not change the output ranking. Previous studies on learning-to-rank either design uni-variate scoring functions that score each document separately, and thus failed to model the cross-document interactions; or construct multivariate scoring functions that score documents sequentially, which inevitably sacrifice the permutation invariance requirement. In this paper, we propose a neural learning-to-rank model called SetRank which directly learns a permutation-invariant ranking model defined on document sets of any size. SetRank employs a stack of (induced) multi-head self attention blocks as its key component for learning the embeddings for all of the retrieved documents jointly. The self-attention mechanism not only helps SetRank to capture the local context information from cross-document interactions, but also to learn permutation-equivariant representations for the inputted documents, which therefore achieving a permutation-invariant ranking model. Experimental results on three large scale benchmarks showed that the SetRank significantly outperformed the baselines include the traditional learning-to-rank models and state-of-the-art Neural IR models.