OpenRaiser/Intern-Atlas huggingface dataset:
Modeling Sequential Collaborative User Behaviors For Seller-Aware Next Basket Recommendation
Authors: Ziyi Kou, Saurav Manchanda, Shih-Ting Lin, Min Xie, Haixun Wang, Xiangliang Zhang
Venue: CIKM
Year: 2023

Next Basket Recommendation (NBR) aims to recommend a set of products as a basket to users based on their historical shopping behavior. In this paper, we investigate the problem of NBR in online marketplaces (e.g., Instacart, Uber Eats) that connect users with multiple sellers. In such scenarios, effective NBR can significantly enhance the shopping experience of users by recommending diversified and completed products based on specific sellers, especially when a user purchases from a seller they have not visited before. However, conventional NBR approaches assume that all considered products are from the same sellers, which overlooks the complex relationships between users, sellers, and products. To address such limitations, we develop SecGT, a sequential collaborative graph transformer framework that recommends users with baskets from specific sellers based on seller-aware user preference representations that are generated by collaboratively modeling the joint user-seller-product interactions and sequentially exploring the user-agnostic basket transitions in an interactive way. We evaluate the performance of SecGT on users from a leading online marketplace at multiple cities with various involved sellers. The results show that SecGT outperforms existing NBR and also traditional product recommendation approaches on recommending baskets from cold sellers for different types of users across all cities.
A Model-Agnostic Method to Interpret Link Prediction Evaluation of Knowledge Graph Embeddings
Authors: Narayanan Asuri Krishnan, Carlos R. Rivero
Venue: CIKM
Year: 2023

In link prediction evaluation, an embedding model assigns plausibility scores to unseen triples in a knowledge graph using an input partial triple. Performance metrics like mean rank are useful to compare models side by side, but do not shed light on their behavior. Interpreting link prediction evaluation and comparing models based on such interpretation are appealing. Current interpretation methods have mainly focused on single predictions or other tasks different from link prediction. Since knowledge graph embedding methods are diverse, interpretation methods that are applicable only to certain machine learning approaches cannot be used. In this paper, we propose a model-agnostic method for interpreting link prediction evaluation as a whole. The interpretation consists of Horn rules mined from the knowledge graph containing the triples a model deems plausible. We combine precision and recall measurements of mined rules using Fβ score to quantify interpretation accuracy. To maximize interpretation accuracy when comparing models, we study two approximations to the hard problem of merging rules. Our quantitative study shows that interpretation accuracy serves to compare diverse models side by side, and that these comparisons are different from those using ranks. Our qualitative study shows that several models globally capture expected semantics, and that models make a common set of predictions despite of redundancy reduction.
Generating Product Insights from Community Q&A
Authors: Lital Kuchy, Ran Levy, Avihai Mejer, Noam Segev, Shunit Agmon, Miriam Farber
Venue: CIKM
Year: 2023

In e-commerce sites, customer questions on the product details-page express the customers' information needs about the product. The answers to these questions often provide the necessary information. In this work, we present and address the novel task of generating product insights from community questions and answers (Q&A). These insights can be presented to customers to assist them in their shopping journey. Our method first generates concise, self-contained sentences based on the information in the Q&A. Then insights are selected based on the prominence of their associated questions. Empirical evaluation attests to the effectiveness of our approach in generating well-formed, objective, and helpful insights that are often not available in the product description or in summaries of customer reviews.
Practical Lessons Learned From Detecting, Preventing and Mitigating Harmful Experiences on Facebook
Authors: Prathyusha Senthil Kumar
Venue: CIKM
Year: 2023

Social media's explosive growth brings with it a variety of societal risks ranging from severely harmful issues such as dangerous organizations and child sexual exploitation to moderately harmful content like displays of aggression, borderline nudity to benign or distasteful contents like gross videos and baity content. In recent times, the multitude and magnitude of these harms is being further exacerbated with the advent of generative AI [5]. Meta is committed to ensuring that Facebook is a place where people feel empowered to communicate and we take our role seriously in keeping abuse off the platform [7]. In this talk, I will describe practical challenges and lessons learned from tackling bad experiences for users on Facebook, particularly in the subjective, borderline and low quality spectrum of harms using state of the art, scalable machine learning approaches to content understanding, user behavior understanding and personalized ranking.
Unsupervised Multi-Modal Representation Learning for High Quality Retrieval of Similar Products at E-commerce Scale
Authors: Kushal Kumar, Tarik Arici, Tal Neiman, Jinyu Yang, Shioulin Sam, Yi Xu, Hakan Ferhatosmanoglu, Ismail B. Tutar
Venue: CIKM
Year: 2023

Identifying similar products in e-commerce is useful in discovering relationships between products, making recommendations, and increasing diversity in search results. Product representation learning is the first step to define a generalized product similarity metric for search. The second step is to extend similarity search to a large scale (e.g., e-commerce catalog scale) without sacrificing quality. In this work, we present a solution that interweaves both steps, i.e., learn representations suited to high quality retrieval using contrastive learning (CL) and retrieve similar items from a large search space using approximate nearest neighbor search (ANNS) to trade-off quality for speed. We propose a CL training strategy for learning uni-modal encoders suited to multi-modal similarity search for e-commerce. We study ANNS retrieval by generating Pareto Frontiers (PFs) without requiring labels. Our CL training strategy doubles retrieval@1 metric across categories (e.g., from 36% to 88% in category C). We also demonstrate that ANNS engine optimization using PFs help select configurations appropriately (e.g., we achieve 6.8× search speed with just 2% drop from the maximum retrieval accuracy in medium size datasets).
Prompting Strategies for Citation Classification
Authors: Suchetha N. Kunnath, David Pride, Petr Knoth
Venue: CIKM
Year: 2023

Citation classification aims to identify the purpose of the cited article in the citing article. Previous citation classification methods rely largely on supervised approaches. The models are trained on datasets with citing sentences or citation contexts annotated for a citation's purpose or function or intent. Recent advancements in Large Language Models (LLMs) have dramatically improved the ability of NLP systems to achieve state-of-the-art performances under zero or few-shot settings. This makes LLMs particularly suitable for tasks where sufficiently large labelled datasets are not yet available, which remains to be the case for citation classification. This paper systematically investigates the effectiveness of different prompting strategies for citation classification and compares them to promptless strategies as a baseline. Specifically, we evaluate the following four strategies, two of which we introduce for the first time, which involve updating Language Model (LM) parameters while training the model: (1) Promptless fine-tuning, (2) Fixed-prompt LM tuning, (3) Dynamic Context-prompt LM tuning (proposed), (4) Prompt + LM fine-tuning (proposed). Additionally, we test the zero-shot performance of LLMs, GPT3.5, a (5) Tuning-free prompting strategy that involves no parameter updating. Our results show that prompting methods based on LM parameter updating significantly improve citation classification performances on both domain-specific and multi-disciplinary citation classifications. Moreover, our Dynamic Context-prompting method achieves top scores both for the ACL-ARC and ACT2 citation classification datasets, surpassing the highest-performing system in the 3C shared task benchmark. Interestingly, we observe zero-shot GPT3.5 to perform well on ACT2 but poorly on the ACL-ARC dataset.
CRUISE-Screening: Living Literature Reviews Toolbox
Authors: Wojciech Kusa, Petr Knoth, Allan Hanbury
Venue: CIKM
Year: 2023

Keeping up with research and finding related work is still a time-consuming task for academics. Researchers sift through thousands of studies to identify a few relevant ones. Automation techniques can help by increasing the efficiency and effectiveness of this task. To this end, we developed CRUISE-Screening, a web-based application for conducting living literature reviews -- a type of literature review that is continuously updated to reflect the latest research in a particular field. CRUISE-Screening is connected to several search engines via an API, which allows for updating the search results periodically. Moreover, it can facilitate the process of screening for relevant publications by using text classification and question answering models. CRUISE-Screening can be used both by researchers conducting literature reviews and by those working on automating the citation screening process to validate their algorithms. The application is open-source, and a demo is available under this URL: https://citation-screening.ec.tuwien.ac.at.
A Re-evaluation of Deep Learning Methods for Attributed Graph Clustering
Authors: Xinying Lai, Dingming Wu, Christian S. Jensen, Kezhong Lu
Venue: CIKM
Year: 2023

Attributed graph clustering aims to partition the nodes in a graph into groups such that the nodes in the same group are close in terms of graph proximity and also have similar attribute values. Recently, deep learning methods have achieved state-of-the-art clustering performance. However, the effectiveness of existing methods remains unclear due to two reasons. First, the datasets used for evaluation do not support fully the goal of attributed graph clustering. The category labels of nodes are only relevant to node attributes, and nodes with the same category label are often distant in the graph. Second, existing methods for the attributed graph clustering are complex and consist of several components. There is lack of comparisons of methods composed of different components from existing methods. This study proposes six benchmark datasets that support better the goal of attributed graph clustering and reports the performance of existing representative methods. Given that existing methods leave room for improvement on the proposed benchmark datasets, we systematically analyze five aspects of existing methods: encoded information, training networks, fusion mechanisms, loss functions, and clustering result generation. Based on these aspects, we decompose existing methods into modules and evaluate the performance of reconfigured methods based on these modules. According to the experimental results on the proposed benchmark datasets, we identify two promising configurations: (i) taking the attribute matrix as input to a graph convolutional network and (ii) layer-wise linear fusing deep neural network and graph attention network. And we also find that complex loss function fails to improve the clustering performance.
VFedAD: A Defense Method Based on the Information Mechanism Behind the Vertical Federated Data Poisoning Attack
Authors: Jinrong Lai, Tong Wang, Chuan Chen, Yihao Li, Zibin Zheng
Venue: CIKM
Year: 2023

In recent years, federated learning has achieved remarkable results in the medical and financial fields, but various attacks have always plagued federated learning. Data poisoning attack and defense research in horizontal federated learning are sufficient, yet vertical federated data poisoning attack and defense remains an open area due to two challenges: (1) Complex data distributions lead to immense attack possibilities, and (2) defense methods are insufficient for complex data distributions. We have discovered that from the perspective of information theory, the above challenges can be addressed elegantly and succinctly with a solution. We first reveal the information-theoretic mechanisms underlying vertical federated data poisoning attacks and then propose an unsupervised vertical federated data poisoning defense method (VFedAD) based on information theory. VFedAD learns semantic-rich client data representations through contrastive learning task and cross-client prediction task to identify anomalies. Experiments show VFedAD effectively detects vertical federated anomalies, protecting subsequent algorithms from vertical federated data poisoning attacks.
Learning the Co-evolution Process on Live Stream Platforms with Dual Self-attention for Next-topic Recommendations
Authors: Hsu-Chao Lai, Philip S. Yu, Jiun-Long Huang
Venue: CIKM
Year: 2023

Live stream platforms have gained popularity in light of emerging social media platforms. Unlike traditional on-demand video platforms, viewers and streamers on the live stream platforms are able to interact in real-time, and this makes viewer interests and live stream topics mutually affect each other on the fly, which is the unique co-evolution phenomenon on live stream platforms. In this paper, we make the first attempt to introduce a novel next-topic recommendation problem for the streamers, LSNR, which incorporates the co-evolution phenomenon. A novel framework CENTR introducing the Co-evolutionary Sequence Embedding Structure that captures the temporal relations of viewer interests and live stream topic sequences with two stacks of self-attention layers is proposed. Instead of learning the sequences individually, a novel dual self-attention mechanism is designed to model interactions between the sequences. The dual self-attention includes two modules, LCA and LVA, to leverage viewer loyalty to improve efficiency and flexibility. Finally, to facilitate cold-start recommendations for new streamers, a collaborative diffusion mechanism is implemented to improve a meta learner. Through the experiments in real datasets, CENTR outperforms state-of-the-art recommender systems in both regular and cold-start scenarios.
The Price is Right: Removing A/B Test Bias in a Marketplace of Expirable Goods
Authors: Thu Le, Alex Deng
Venue: CIKM
Year: 2023

Pricing Guidance tools at Airbnb aim to help hosts maximize the earning for each night of stay. For a given listing, the earning-maximization price point of a night can vary greatly with lead-day - the number of days from now until the night of stay. This introduces systematic bias in running marketplace A/B tests to compare the performances of two pricing strategies. Lead-day bias can cause the short-term experiment result to move in the opposite direction to the long-term impact, possibly leading to the suboptimal business decision and customer dissatisfaction. We propose an efficient experimentation approach that corrects for the bias, minimizes the possible negative impact of experimenting, and greatly accelerates the R&D cycle. This paper is the first of its kind to lays out the theoretical framework along with the real-world example that demonstrates the magnitude of the bias. It serves as a conversation starter for such insidious type of experimentation bias that is likely present in other marketplaces of expirable goods such as vacation nights, car rentals, and airline tickets, concert passes, or ride-hailings.
You're Not Alone in Battle: Combat Threat Analysis Using Attention Networks and a New Open Benchmark
Authors: Soo Yong Lee, Juwon Kim, Kiwoong Park, Dong Kuk Ryu, Sang Heun Shim, Kijung Shin
Venue: CIKM
Year: 2023

For military commands, combat threat analysis is crucial in predicting future outcomes and informing consequent decisions. Its primary objectives include determining the intention and attack likelihood of the hostiles. The complex, dynamic, and noisy nature of combat, however, presents significant challenges in its analysis. The prior research has been limited in accounting for such characteristics, assuming independence of each entity, no unobserved tactics, and clean combat data. As such, we present spatio-temporal attention for threat analysis (SAFETY) to encode complex interactions that arise within combat. We test the model performance for unobserved tactics and with various perturbations. To do so, we also present the first open-source benchmark for combat threat analysis with two downstream tasks of predicting entity intention and attack probability. Our experiments show that SAFETY achieves a significant improvement in model performance, with enhancements of up to 13% in intention prediction and 7% in attack prediction compared to the strongest competitor, even when confronted with noisy or missing data. This result highlights the importance of encoding dynamic interactions among entities for combat threat analysis. Our codes and dataset are available at https://github.com/syleeheal/SAFETY.
DuoGAT: Dual Time-oriented Graph Attention Networks for Accurate, Efficient and Explainable Anomaly Detection on Time-series
Authors: Jongsoo Lee, Byeongtae Park, Dong-Kyu Chae
Venue: CIKM
Year: 2023

Recently, Graph Neural Networks (GNNs) have achieved state-of-the-art performance on the multivariate time-series anomaly detection task by learning relationships between variables (sensors). However, they show limitations in capturing temporal dependencies due to lack of sufficient consideration on the characteristics of time to their graph structure. Several studies constructed a time-oriented graph, where each node represents a timestamp within a certain sliding window, to model temporal dependencies, but they failed to learn the trend of changes in time-series. This paper proposes Dual time-oriented Graph ATtention networks (DuoGAT) that resolves the aforementioned problems. Unlike previous work that uses the simple complete undirected structure for time-oriented graphs, our work models directed graphs with weighted edges that only connect from prior events to posterior events, and the edges that connect nearby events are given higher weights. In addition, another time-oriented graph is used to model time series stationary via differencing, which especially focuses on capturing the series of changes. Empirically, our method outperformed the existing state-of-the-art work with the highest F1-score for the four real-world dataset while maintaining low training cost. We also proposed a novel explanation method for anomaly detection using DuoGAT, which provides time-oriented reasoning via hierarchically tracking time points critical in a specific anomaly detection. Our code is available at: https://github.com/ByeongtaePark/DuoGAT
Application of Deep Clustering Algorithms
Authors: Collin Leiber, Lukas Miklautz, Claudia Plant, Christian Böhm
Venue: CIKM
Year: 2023

Deep clustering algorithms have gained popularity for clustering complex, large-scale data sets, but getting started is difficult because of numerous decisions regarding architecture, optimizer, and other hyperparameters. Theoretical foundations must be known to obtain meaningful results. At the same time, ease of use is necessary to get used by a broader audience. Therefore, we require a unified framework that allows for easy execution in diverse settings. While this applies to established clustering methods like k-Means and DBSCAN, deep clustering algorithms lack a standard structure, resulting in significant programming overhead. This complicates empirical evaluations, which are essential in both scientific and practical applications. We present a solution to this problem by providing a theoretical background on deep clustering as well as practical implementation techniques and a unified structure with predefined neural networks. For the latter, we use the Python package ClustPy. The aim is to share best practices and facilitate community participation in deep clustering research.
Harnessing the Power of Pre-trained Vision-Language Models for Efficient Medical Report Generation
Authors: Qi Li
Venue: CIKM
Year: 2023

Medical images are commonly used in clinical practice. But the need for diagnosis and reporting from image-based examinations far excels the current medical capacity. Automatic Medical Report Generation (MRG) can help to ease the burden of radiologists. Vision-Language Pre-training (VLP) has received tremendous success on various tasks, therefore it is naturally expected that MRG can harvest from this rapid advancement. However, directly applying existing VLP models in the medical domain is impracticable due to their data-hungry nature, the need for aligning different modalities, prohibitive training time, exorbitant hardware barrier, and the challenge of open-ended text generation. To address these problems, we propose MedEPT, a parameter-efficient approach for MRG that can utilize ever-ignored image-only datasets. It employs parameter-efficient tuning (PET) for VLP adaption to mitigate inefficiency in fine-tuning time and hardware. MedEPT also employs MRGPID to augment and expand adaption datasets by synthesizing meaningful text for image-only datasets. We perform a systematic evaluation of our method. Empirical results show that we obtain a better performance than the state-of-the-art method while using less than 10% trainable parameters and not more than 30% training time than ever before.
CORD: A Three-Stage Coarse-to-Fine Framework for Relation Detection in Knowledge Base Question Answering
Authors: Yanzeng Li, Sen Hu, Wenjuan Han, Lei Zou
Venue: CIKM
Year: 2023

As a fundamental subtask of Knowledge Base Question Answering (KBQA), Relation Detection (KBQA-RD) plays a crucial role to detect the KB relations between entities or variables in natural language questions. It remains, however, a challenging task, particularly for significant large-scale relations and in the presence of easily confused relations. Recent state-of-the-art methods not only struggle with such scenarios, but often take into account only one facet and fail to incorporate the subtle discrepancy among the relations. In this paper, we propose a simple and efficient three-stage framework to exploit the coarse-to-fine paradigm. Specifically, we employ a natural clustering over all KB relations and perform a coarse-to-fine relation recognition process based on the relation clustering. In this way, our framework (i.e., CORD) refines the detection of relations, so as to scale well with large-scale relations. Experiments on both single-relation (i.e., SimpleQuestions (SQ)) and multi-relation (i.e., WebQSP (WQ)) benchmarks show that CORD not only achieves the outstanding relation detection performance in KBQA-RD subtask; but more importantly, further improves the accuracy of KBQA systems.