OpenRaiser/Intern-Atlas huggingface dataset:
Empowering LLMs for Multi-Page Layout Generation via Consistency-Oriented In-Context Learning
Authors: Mengyao Chen, Xinghua Zhang, Junhao Zhang, Quangang Li, Tingwen Liu
Venue: CIKM
Year: 2024

Document layout generation, a burgeoning field of document intelligence, entails positioning and sizing various elements within given constraints. While significant strides have been made in single-page layout generation, real-world documents predominantly span multiple pages, and exploring multi-page layout generation methods has also become the key to meeting the contemporary dramatically increased document processing demands. Despite the promise of leveraging large language models (LLMs) like GPT-4 for their powerful in-context learning abilities, the task transition to multi-page layouts, which contains considerably complex data, presents formidable challenges including excessively long prompts and strict consistency between pages. To this end, we propose a novel framework called Multi-Page Layout Generation via Consistency-Oriented modeling (MuLCO) that capitalizes on in-context learning of LLMs without the need for training or fine-tuning. MuLCO employs three key components: serialization based on code blocks maps intricate document layouts to code-style exemplars, self-correcting reasoning hint decomposes the complex generation task into numerous steps to improve reasoning interpretability, and consistency-oriented multi-round generation predicts coherent multi-page layouts in form of a continuous dialogue. To summarize, we contribute by proposing MuLCO and developing a task-specific dataset and evaluation mechanism. Extensive experiments validate the effectiveness of the MuLCO framework for multi-page layout generation.
Feedback Reciprocal Graph Collaborative Filtering
Authors: Weijun Chen, Yuanchen Bei, Qijie Shen, Hao Chen, Xiao Huang, Feiran Huang
Venue: CIKM
Year: 2024

Collaborative filtering on user-item interaction graphs has achieved success in the industrial recommendation. However, recommending users' truly fascinated items poses a seesaw dilemma for collaborative filtering models learned from the interaction graph. On the one hand, not all items that users interact with are equally appealing. Some items are genuinely fascinating to users, while others are unfascinated. Training graph collaborative filtering models in the absence of distinction between them can lead to the recommendation of unfascinating items to users. On the other hand, disregarding the interacted but unfascinating items during graph collaborative filtering will result in an incomplete representation of users' interaction intent, leading to a decline in the model's recommendation capabilities. To address this seesaw problem, we propose Feedback Reciprocal Graph Collaborative Filtering (FRGCF), which emphasizes the recommendation of fascinating items while attenuating the recommendation of unfascinating items. Specifically, FRGCF first partitions the entire interaction graph into the Interacted & Fascinated (I&F) graph and the Interacted & Unfascinated (I&U) graph based on the user feedback. Then, FRGCF introduces separate collaborative filtering on the I&F graph and the I&U graph with feedback-reciprocal contrastive learning and macro-level feedback modeling. This enables the I&F graph recommender to learn multi-grained interaction characteristics from the I&U graph without being misdirected by it. Extensive experiments on four benchmark datasets and a billion-scale industrial dataset demonstrate that FRGCF improves the performance by recommending more fascinating items and fewer unfascinating items. Besides, online A/B tests on Taobao's recommender system verify the superiority of FRGCF.
PP4RNR: Popularity- and Position-Aware Contrastive Learning for Retrieval-Driven News Recommendation
Authors: Wenwei Chen, Yewang Chen
Venue: CIKM
Year: 2024

Existing news recommendation systems often overlook the diversity of recommended content and exhibit popularity bias, resulting in suboptimal performance. To address this issue, this paper introduces a novel news recommendation approach, Popularity- and Position-Aware Contrastive Learning for Retrieval-Driven News Recommendation (PP4RNR). It consists of two modules: Entity-Level Retrieval Augmentation (ERA) and Popularity- and Position-Aware Contrastive Learning (PPCL). The ERA module utilizes both entities and titles to retrieve relevant news. Subsequently, retrieval-augmented news is fused with candidate news using our innovative cascaded attention network, leading to richer and more diverse news semantics. The PPCL module introduces perturbations in the news representation using a Gaussian perturbation vector based on the popularity and position information and then employs contrastive learning to regularize the representation space. Hence, this approach not only deepens the understanding of content diversity but also implicitly mitigates the popularity bias prevalent in current models. Rigorous testing on benchmark datasets demonstrates that our method significantly outperforms a range of state-of-the-art techniques.
Domain Alignment with Large Vision-language Models for Cross-domain Remote Sensing Image Retrieval
Authors: Yan Chen, Guocan Cai, Fufang Li, Yangtao Wang, Xin Tan, Xiaocui Li
Venue: CIKM
Year: 2024

Cross-domain remote sensing image retrieval has been a hotspot in the past few years. Most of the existing methods focus on combining semantic learning with domain adaptation on well-labeled source domain and unlabeled target domain. However, they face two serious challenges. (1) They cannot deal with practical scenarios where the source domain lacks sufficient label supervision. (2) They suffer from severe performance degradation when the data distribution between the source domain and target domain becomes highly inconsistent. To address these challenges, we propose D omain A lignment with L arge V ision-language models for cross-domain remote sensing image retrieval (termed as DALV). First, we design a dual-modality prototype guided pseudo-labeling mechanism, which leverages the pre-trained large vision-language model (i.e., CLIP) to assign pseudo-labels for all unlabeled source domain images and target domain images. Second, we compute the confidence scores for these pseudo-labels to distinguish their reliability. Next, we devise a loss reweighting strategy, which incorporates the confidence scores as weight values into the contrastive loss to mitigate the impact of noisy pseudo-labels. Finally, the low-rank adaptation fine-tuning means is adapted to update our model and achieve domain alignment to obtain class discriminative features. Extensive experiments on 12 cross-domain remote sensing image retrieval tasks show that our proposed DALV outperforms the state-of-the-art approaches. The source code is available at https://github.com/ptyy01/DALV.
ELCoRec: Enhance Language Understanding with Co-Propagation of Numerical and Categorical Features for Recommendation
Authors: Jizheng Chen, Kounianhua Du, Jianghao Lin, Bo Chen, Ruiming Tang, Weinan Zhang, Yong Yu
Venue: CIKM
Year: 2024

Large language models have been flourishing in the natural language processing (NLP) domain, and their potential for recommendation has been paid much attention to. Despite the intelligence shown by the recommendation-oriented finetuned models, LLMs struggle to fully understand the user behavior patterns due to their innate weakness in interpreting numerical features and the overhead for long context, where the temporal relations among user behaviors, subtle quantitative signals among different ratings, and various side features of items are not well explored. Existing works only fine-tune a sole LLM on given text data without introducing that important information to it, leaving these problems unsolved. In this paper, we propose ELCoRec to Enhance Language understanding with Co-Propagation of numerical and categorical features for Recommendation. Concretely, we propose to inject the preference understanding capability into LLM via a GAT expert model where the user preference is better encoded by parallelly propagating the temporal relations, and rating signals as well as various side information of historical items. The parallel propagation mechanism could stabilize heterogeneous features and offer an informative user preference encoding, which is then injected into the language models via soft prompting at the cost of a single token embedding. To further obtain the user's recent interests, we proposed a novel Recent interaction Augmented Prompt (RAP) template. Experiment results over three datasets against strong baselines validate the effectiveness of ELCoRec.
PACIFIC: Enhancing Sequential Recommendation via Preference-aware Causal Intervention and Counterfactual Data Augmentation
Authors: Jinpeng Chen, Huachen Guan, Huan Li, Fan Zhang, Liwei Huang, Guangyao Pang, Xiongnan Jin
Venue: CIKM
Year: 2024

Sequential recommendation has been receiving increasing attention from researchers. Existing sequential recommendation models leverage deep learning models to capture sequential features. However, these methods ignore confounders in the recommendation process, which can lead the model to learn incorrect correlations and fail to accurately capture users' true preferences. Moreover, these methods rely on extensive interaction sequences, but sequential data often suffers from sparsity issues. To address these limitations, this paper proposes a P reference- a ware C ausal I ntervention and Counter f a c tual Data Augmentation ( Pacific ) framework to enhance sequential recommendation. Initially, we model the causal graph of sequential recommendation and categorize user preferences into global long-term preferences, local long-term preferences, and short-term preferences. Then, we introduce the front-door criterion to eliminate the interference of confounders and design different self-attention mechanisms to estimate the causal effects, aiming to capture users' true preferences. In addition, based on counterfactual thinking, we design a counterfactual data augmentation module to generate enriched sequences. Experimental results on four real-world datasets demonstrate the superiority of our proposed approach over state-of-the-art sequential recommendation methods.
Missing Interest Modeling with Lifelong User Behavior Data for Retrieval Recommendation
Authors: Gaode Chen, Yuezihan Jiang, Rui Huang, Kuo Cai, Yunze Luo, Ruina Sun, Qi Zhang, Han Li, Kun Gai
Venue: CIKM
Year: 2024

Rich user behavior data has been proven to be of great value for recommendation systems. Modeling lifelong user behavior data in the retrieval stage to explore user long-term preference and obtain comprehensive retrieval results is crucial. Existing lifelong modeling methods cannot applied to the retrieval stage because they extract target-relevant items through the coupling between the user and the target item. Moreover, the current retrieval methods fail to precisely capture user interests when the length of the user behavior sequence increases further. That leads to a gap in the ability of retrieval models to model lifelong user behavior data. In this paper, we propose the concept of missing interest, leveraging the idea of complementarity, which serves as a supplement to short-term interest based on lifelong behavior data in the retrieval stage. Specifically, we design a missing interest operator and deploy it in Kafka data stream, without incurring latency or storage costs. This operator derives categories and authors of items that the user was previously interested in but has recently missed, and uses these as triggers to output missing features to the downstream retrieval model. Our retrieval model is a complete dual-tower structure that combines short-term and missing interests on the user side to provide a comprehensive depiction of lifelong behaviors. Since 2023, the presented solution has been deployed in Kuaishou, one of the most popular short-video streaming platforms in China with hundreds of millions of active users.
Hands-On Introduction to Quantum Machine Learning
Authors: Samuel Yen-Chi Chen, Joongheon Kim
Venue: CIKM
Year: 2024

This tutorial offers a hands-on introduction into the captivating field of quantum machine learning (QML). Beginning with the bedrock of quantum information science (QIS)-including essential elements like qubits, single and multiple qubit gates, measurements, and entanglement-the session swiftly progresses to foundational QML concepts. Participants will explore parameterized or variational circuits, data encoding or embedding techniques, and quantum circuit design principles. Delving deeper, attendees will examine various QML models, including the quantum support vector machine (QSVM), quantum feed-forward neural network (QNN), and quantum convolutional neural network (QCNN). Pushing boundaries, the tutorial delves into cutting-edge QML models such as quantum recurrent neural networks (QRNN) and quantum reinforcement learning (QRL), alongside privacy-preserving techniques like quantum federated machine learning, bolstered by concrete programming examples. Throughout the tutorial, all topics and concepts are brought to life through practical demonstrations executed on a quantum computer simulator. Designed with novices in mind, the content caters to those eager to embark on their journey into QML. Attendees will also receive guidance on further reading materials, as well as software packages and frameworks to explore beyond the session.
Social Influence Learning for Recommendation Systems
Authors: Ximing Chen, Pui Ieng Lei, Yijun Sheng, Yanyan Liu, Zhiguo Gong
Venue: CIKM
Year: 2024

Social recommendation systems leverage the social relations among users to deal with the inherent cold-start problem in user-item interactions. However, previous models only treat the social graph as the static auxiliary to the user-item interaction graph, rather than dig out the hidden essentials and optimize them for better recommendations. Thus, the potential of social influence is still under-explored. In this paper, we will fill this gap by proposing a novel model for social influence learning to derive the essential influence patterns within the user relationships. Our model views the social influence from the perspectives of (1) the diversity of neighborhood's influence on the users, (2) the disentanglement of neighborhood's influence on the users, and (3) the exploration of underlying implicit social influence. To this end, we first employ a novel layerwise graph-enhanced variational autoencoder for the reconstruction of neighborhoods' representations, which aims to learn the pattern of social influence as well as simulate the social profile of each user for overcoming the sparsity issue in social relation data. Meanwhile, we introduce a layerwise graph attentive network for capturing the most influential scope of neighborhood. Finally, we adopt a dual sampling process to generate new social relations for enhancing the social recommendation. Extensive experiments have been conducted on three widely-used benchmark datasets, verifying the superiority of our proposed model compared with the representative approaches.
PlayBest: Professional Basketball Player Behavior Synthesis via Planning with Diffusion
Authors: Xiusi Chen, Wei-Yao Wang, Ziniu Hu, David Reynoso, Kun Jin, Mingyan Liu, P. Jeffrey Brantingham, Wei Wang
Venue: CIKM
Year: 2024

Dynamically planning in complex systems has been explored to improve decision-making in various domains. Professional basketball serves as a compelling example of a dynamic spatio-temporal game, encompassing context-dependent decision-making. However, processing the diverse on-court signals and navigating the vast space of potential actions and outcomes make it difficult for existing approaches to swiftly identify optimal strategies in response to evolving circumstances. In this study, we formulate the sequential decision-making process as a conditional trajectory generation process. Based on the formulation, we introduce PlayBest (PLAYer BEhavior SynThesis), a method to improve player decision-making. We extend the diffusion probabilistic model to learn challenging environmental dynamics from historical National Basketball Association (NBA) player motion tracking data. To incorporate data-driven strategies, an auxiliary value function is trained with corresponding rewards. To accomplish reward-guided trajectory generation, we condition the diffusion model on the value function via classifier-guided sampling. We validate the effectiveness of PlayBest through simulation studies, contrasting the generated trajectories with those employed by professional basketball teams. Our results reveal that the model excels at generating reasonable basketball trajectories that produce efficient plays. Moreover, the synthesized play strategies exhibit an alignment with professional tactics, highlighting the model's capacity to capture the intricate dynamics of basketball games.
Trustworthy and Responsible AI for Information and Knowledge Management System
Authors: Huaming Chen, Jun Zhuang, Yu Yao, Wei Jin, Haohan Wang, Yong Xie, Chi-Hung Chi, Kim-Kwang Raymond Choo
Venue: CIKM
Year: 2024

The way research and business manage and utilize knowledge is undergoing a significant transformation, driven by Artificial Intelligence (AI). Deep learning and machine learning are emerging as powerful tools for optimizing knowledge management systems, leading to more informed and productive development. AI offers unique solutions for organizations struggling with information overload and inefficient knowledge transfer. These AI models can significantly improve data management and utilization. Imagine an AI-powered system that streamlines onboarding processes, provides precise answers to various queries, and even captures the valuable tacit knowledge (implicit skills and expertise) often residing within individuals. AI bridges the gap between explicit knowledge (easily documented information) and tacit knowledge, fostering a more comprehensive and accessible knowledge base. However, such AI systems solicit trustworthy and responsible approaches to mitigate potential misuse and malfunction. In this workshop, we aim to gather researchers and engineers from academia and industry to discuss the latest advances in trustworthy and responsible AI solutions for information and knowledge management systems.
CMG: A Causality-enhanced Multi-view Graph Model for Stock Trend Prediction
Authors: Xi Cheng, Liang Wang, Yunan Zeng, Qiang Liu
Venue: CIKM
Year: 2024

The stock trend prediction problem refers to forecasting future stock price trends. In recent years, some methods discovered causal relations between stocks to address this problem. However, traditional causal discovery methods face unique challenges in the stock market, as they fail to uncover accurate causal relationships when a distribution shift happens in stock. Additionally, current methods also overlook the commonalities and differences between stock relations. To address these shortcomings, we propose a causal-enhanced multi-view temporal graph model, named CMG. This method explores comprehensive causal relations by incorporating distribution shift confounder and constructs a multi-view contrastive learning module to unearth the commonalities and differences between stock relations, thereby enabling more accurate stock trend predictions. Further experimental results and investment simulations demonstrate the effectiveness and profitability of CMG.
MODRL-TA: A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search
Authors: Peng Cheng, Huimu Wang, Jinyuan Zhao, Yihao Wang, Enqiang Xu, Yu Zhao, Zhuojian Xiao, Songlin Wang, Guoyu Tang, Lin Liu, Sulong Xu
Venue: CIKM
Year: 2024

Traffic allocation is a process of redistributing natural traffic to products by adjusting their positions in the post-search phase, aimed at effectively fostering merchant growth, precisely meeting customer demands, and ensuring the maximization of interests across various parties within e-commerce platforms. Existing methods based on learning to rank neglect the long-term value of traffic allocation, whereas approaches of reinforcement learning suffer from balancing multiple objectives and the difficulties of cold starts within real-world data environments. To address the aforementioned issues, this paper propose a multi-objective deep reinforcement learning framework consisting of multi-objective Q-learning (MOQ), a decision fusion algorithm (DFM) based on the cross-entropy method(CEM), and a progressive data augmentation system (PDA). Specifically. MOQ constructs ensemble RL models, each dedicated to an objective, such as click-through rate, conversion rate, etc. These models individually determine the position of items as actions, aiming to estimate the long-term value of multiple objectives from an individual perspective. Then we employ DFM to dynamically adjust weights among objectives to maximize long-term value, addressing temporal dynamics in objective preferences in e-commerce scenarios. Initially, PDA trained MOQ with simulated data from offline logs. As experiments progressed, it strategically integrated real user interaction data, ultimately replacing the simulated dataset to alleviate distributional shifts and the cold start problem. Experimental results on real-world online e-commerce systems demonstrate the significant improvements of MODRL-TA, and we have successfully deployed MODRL-TA on an e-commerce search platform.
1st Workshop on Multimodal Search and Recommendations (CIKM MMSR '24)
Authors: Aditya Chichani, Surya Kallumadi, Tracy Holloway King, Andrei Lopatenko
Venue: CIKM
Year: 2024

With the advent of multimodal LLMs and release of open-source multimodal models, the potential for multimodal search and recommendations has significantly increased. Multimodal systems offer a next-gen customer experience by creating a shared embedding space for text, images, audio, etc. These advancements enable more accurate, personalized recommendations, enhancing user satisfaction and engagement. This workshop on Multimodal Search and Recommendations explores the latest advancements, challenges, and applications of multimodal search and recommendations.
BioMAISx: A Corpus for Aspect-Based Sentiment Analysis of Media Representations of Agricultural Biotechnologies in Africa
Authors: Patricia Chiril, Trevor Spreadbury, Joeva Rock, Brian Dowd-Uribe, David Uminsky
Venue: CIKM
Year: 2024

News articles constitute a valuable resource for opinion mining, as they contain important perspectives related to the subject matter they cover. In this paper, we explore how aspect-based sentiment analysis might help in understanding the public discourse surrounding agricultural biotechnologies in Africa. We introduce BioMAISx, the first English language dataset composed of direct quotes pertaining to agricultural biotechnologies extracted from a curated list of Africa-based news sources. We have identified and labelled entities related to key aspects of agricultural biotechnologies, providing valuable insights into public discourse. This dataset can aid in identifying challenges, improving public discourse, and monitoring the perception of agricultural biotechnologies, thus contributing to informed decision-making.