OpenRaiser/Intern-Atlas huggingface dataset:
IMinimize: A System for Negative Influence Minimization via Vertex Blocking
Authors: Siyi Teng, Jiadong Xie, Mingkai Zhang, Kai Wang, Fan Zhang
Venue: CIKM
Year: 2023

The rapid rise and prevalence of social platforms have created great demands on effective schemes to limit the influence of negative information, e.g., blocking key vertices for influence minimization. However, there is currently no system providing practical schemes to solve the negative influence minimization problem with a blocking budget effectively and efficiently in the literature. In this demo, we present IMinimize, the first interactive system that provides audiences with vertex-blocking schemes over different budgets and demonstrates via visualization for comparison vividly and directly, aiming to help minimize the negative influence spreading in networks. Our IMinimize system applies an advanced greedy algorithm to select blocked vertices with both high efficiency and effectiveness. Furthermore, we extend IMinimize to the application of epidemic controlling and prevention and show the usability of IMinimize through two case studies of real-life applications.
PRODIGY: Product Design Guidance at Scale
Authors: Sambeet Tiady, Anirban Majumder, Deepak Gupta
Venue: CIKM
Year: 2023

Growth of e-commerce has enabled the creation of thousands of small-scale brands. However, these brands lack information on a) what new products to develop and b) how to refine existing products to improve on business metrics. We present a comprehensive Product Design Insights and Guidance service (named PRODIGY) that mines product attributes data available on e-commerce platforms and surface insights on a) new product development and b) product refinement. Our core contribution is a novel demand forecasting model for product designs based on a notable extension of the recently proposed FTTransformer architecture combined with a self-supervised pre-training task, akin to Masked Language Modeling (MLM) objective. For the product refinement use-case, we present a novel algorithm by embedding the design search in a data-density approximator, namely Conditional Variational Autoencoder. We run a thorough and comprehensive set of experiments and establish that PRODIGY achieves significant improvement in demand prediction as compared to state-of-the-art alternatives. Finally, we present our findings from an online experiment where PRODIGY helps to launch new products with +20% lift in sales and +1.3% lift in product ratings.
Periodicity May Be Emanative: Hierarchical Contrastive Learning for Sequential Recommendation
Authors: Changxin Tian, Binbin Hu, Wayne Xin Zhao, Zhiqiang Zhang, Jun Zhou
Venue: CIKM
Year: 2023

Nowadays, contrastive self-supervised learning has been widely incorporated into sequential recommender systems. However, most existing contrastive sequential recommender systems simply emphasize the overall information of interaction sequences, thereby neglecting the special periodic patterns of user behavior. In this study, we propose that users exhibit emanative periodicity towards a group of correlated items, i.e., user behavior follow a certain periodic pattern while their interests may shift from one item to other related items over time. In light of this observation, we present a hierarchical contrastive learning framework to model EmAnative periodicity for SEquential Recommendation (referred to as EASE). Specifically, we design dual-channel contrastive strategy from the perspective of correlation and periodicity to capture emanative periodic patterns. Furthermore, we extend the traditional binary contrastive loss with hierarchical constraint to handle hierarchical contrastive samples, thus preserving the inherent hierarchical information of correlation and periodicity. Comprehensive experiments conducted on five datasets substantiate the effectiveness of our proposed EASE in improving sequential recommendation.
Multi-Representation Variational Autoencoder via Iterative Latent Attention and Implicit Differentiation
Authors: Nhu-Thuat Tran, Hady W. Lauw
Venue: CIKM
Year: 2023

Variational Autoencoder (VAE) offers a non-linear probabilistic modeling of user's preferences. While it has achieved remarkable performance at collaborative filtering, it typically samples a single vector for representing user's preferences, which may be insufficient to capture the user's diverse interests. Existing solutions extend VAE to model multiple interests of users by resorting a variant of self-attentive method, i.e., employing prototypes to group items into clusters, each capturing one topic of user's interests. Despite showing improvements, the current design could be more effective since prototypes are randomly initialized and shared across users, resulting in uninformative and non-personalized clusters. To fill the gap, firstly, we introduce iterative latent attention for personalized item grouping into VAE framework to infer multiple interests of users. Secondly, we propose to incorporate implicit differentiation to improve training of our iterative refinement model. Thirdly, we study the self-attention to refine cluster prototypes for item grouping, which is largely ignored by existing works. Extensive experiments on three real-world datasets demonstrate stronger performance of our method over those of baselines.
Citation Intent Classification and Its Supporting Evidence Extraction for Citation Graph Construction
Authors: Hong-Jin Tsai, An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: CIKM
Year: 2023

As the significant growth of scientific publications in recent years, an efficient way to extract scholarly knowledge and organize the relationship among literature is necessitated. Previous works constructed scientific knowledge graph with authors, papers, citations, and scientific entities. To assist researchers to grasp the research context comprehensively, this paper constructs a fine-grained citation graph in which citation intents and their supporting evidence are labeled between citing and cited papers instead. We propose a model with a Transformer encoder to encode the long-lengthy paper. To capture the coreference relations of words and sentences in a paper, a coreference graph is created by utilizing Gated Graph Convolution Network (GGCN). We further propose a graph modification mechanism to dynamically update the coreference links. Experimental results show that our model achieves promising results on identifying multiple citation intents in sentences.
Disentangled Interest importance aware Knowledge Graph Neural Network for Fund Recommendation
Authors: Ke Tu, Wei Qu, Zhengwei Wu, Zhiqiang Zhang, Zhongyi Liu, Yiming Zhao, Le Wu, Jun Zhou, Guannan Zhang
Venue: CIKM
Year: 2023

At present, people are gradually becoming aware of financial management and thus fund recommendation attracts more and more attention to help them find suitable funds quickly. As a user usually takes many factors (e.g., fund theme, fund manager) into account when investing a fund and the fund usually consists of a substantial collection of investments, effectively modeling multi-interest representations is more crucial for personalized fund recommendation than the traditional goods recommendation. However, existing multi-interest methods are largely sub-optimal for fund recommendation, since they ignore financial domain knowledge and diverse fund investment intentions. In this work, we propose a Disentangled Interest importance aware Knowledge Graph Neural Network (DIKGNN) for personalized fund recommendation on FinTech platforms. In particular, we restrict the multiple intent spaces by introducing the attribute nodes from the fund knowledge graph as the minimum intent modeling unit to utilize financial domain knowledge and provide interpretability. In the intent space, we define disentangled intent representations, equipped with intent importance distributions to describe the diverse fund investment intentions. Then we design a new neighbor aggregation mechanism with the learned intent importance distribution upon the interaction graph and knowledge graph to collect multi-intent information. Furthermore, we leverage micro independence and macro balance constraints on the representations and distributions respectively to encourage intent independence and diversity. The extensive experiments on public recommendation benchmarks demonstrate that DIKGNN can achieve substantial improvement over state-of-the-art methods. Our proposed model is also evaluated over one real-world industrial fund dataset from a FinTech platform and has been deployed online.
Prod2Vec-Var: A Session Based Recommendation System with Enhanced Diversity
Authors: Hacer Turgut, Tan Doruk Yetki, Ömür Bali, Tayfun Arda Yücel
Venue: CIKM
Year: 2023

Understanding user behavior and leveraging this information in recommendation systems pose challenges for websites lacking a login system or with limited logged-in users. This study introduces the Prod2Vec-Var recommendation system, a modified version of a session-based recommendation algorithm aimed at enhancing the performance of product recommendation systems with a cold-start extension. The proposed model builds upon the original prod2vec algorithm, incorporating an additional step to improve the diversity of product recommendations. The project entails a well-designed data pipeline, effectively processing user actions to align them with the model, and implementing unique functions that expand the range of products capturing users' attention. Moreover, a straightforward yet effective cold-start model is developed to address newly added products that have not been viewed by users. The outcome of our project, namely product suggestions, is presented to users of cimri.com, one of iLab's affiliated companies, which attracts millions of daily visits, thereby enabling seamless access to desired products. Experimental results demonstrated the superior performance of our model compared to the other two different strategies in running recommendation on popular products, as evidenced by favorable R@1, R@5, R@10, and R@15 metrics. Concerning less popular products, we observed an improvement in our model's performance as the value of K increased, ultimately achieving optimal results in terms of R@15. Additionally, our cold-start model for new products substantiated the efficacy of our methodology, yielding the highest scores across R@5, R@10, and R@15 metrics.
Generating Optimized Molecules without Patent Infringement
Authors: Sally Turutov, Kira Radinsky
Venue: CIKM
Year: 2023

Molecular optimization seeks to improve a given molecule's therapeutic profile. It is a key challenge in drug development, but it is difficult due to the constraints of molecular similarity to the original molecule and the size of the chemical space to explore. Numerous works tackled this problem with initial success. Unlike previous works that focus on generating molecules that optimize chemical properties, we focus on the optimization while attempting to "move away" from patented molecules. We present a novel loss function and its utilization in numerous types of molecular optimization algorithms. The loss allows to improve molecular properties while decreasing patent-infringement. We perform empirical evaluation showing superior performance of state-of-the-art models when using the novel loss function. The deployment of the system is underway at the Targeted Drug Delivery and Personalized Medicine labs. It will be utilized to generate targeted carriers of mRNA, providing a new method of drug delivery. The system is also producing non-patented candidates for industrial use, making it a valuable tool in the field of personalized medicine.
Cluster-Explorer: An interactive Framework for Explaining Black-Box Clustering Results
Authors: Sariel Tutay, Amit Somech
Venue: CIKM
Year: 2023

Interpreting clustering results is a challenging, manual task, that often requires the user to perform additional analytical queries and visualizations. To this end, we demonstrate Cluster-Explorer, an interactive, easy-to-use framework that provides explanations for black-box clustering results. Cluster-Explorer takes as input the raw dataset alongside cluster labels, and automatically generates multiple coherent explanations that characterize each cluster. We first propose a threefold quality measure that considers the conciseness, cluster coverage, and separation error of an explanation. We tackle the challenge of efficiently computing high-quality explanations using a modified version of a generalized frequent-itemsets mining (gFIM) algorithm. The gFIM algorithm is employed over multiple filter predicates which are extracted by applying various binning methods of different granularities. We implemented Cluster-Explorer as a Python library that can be easily used by data scientists in their ongoing workflows. After employing the clustering pipeline of their choice, Cluster-Explorer opens an integrated, interactive interface for the user to explore the various different explanations for each cluster. In our demonstration, the audience is invited to use Cluster-Explorer on numerous real-life datasets and different clustering pipelines and examine the usefulness of the cluster explanations provided by the system, as well as its efficiency of computation.
Knowledge-driven Analytics and Systems Impacting Human Quality of Life- Neurosymbolic AI, Explainable AI and Beyond
Authors: Arijit Ukil, João Gama, Antonio J. Jara, Leandro Marín
Venue: CIKM
Year: 2023

The management of knowledge-driven artificial intelligence technologies is essential in order to evaluate their impact on human life and society. Social networks and tech use can have a negative impact on us physically, emotionally, socially and mentally. On the other hand, intelligent systems can have a positive effect on people's lives. Currently, we are witnessing the power of large language models (LLMs) like chatGPT and its influence towards the society. The objective of the workshop is to contribute to the advancement of intelligent technologies designed to address the human condition. This could include precise and personalized medicine, better care for elderly people, reducing private data leaks, using AI to manage resources better, using AI to predict risks, augmenting human capabilities, and more. The workshop's objective is to present research findings and perspectives that demonstrate how knowledge-enabled technologies and applications improve human well-being. This workshop indeed focuses on the impacts at different granularity levels made by Artificial Intelligence (AI) research on the micro granular level, where the daily or regular functioning of human life is affected, and also the macro granulate level, where the long-term or far-future effects of artificial intelligence on people's lives and the human society could be pretty high. In conclusion, this workshop explores how AI research can potentially address the most pressing challenges facing modern societies, and how knowledge management can potentially contribute to these solutions.
Explaining Learning to Rank Methods to Improve Them
Authors: Alberto Veneri
Venue: CIKM
Year: 2023

State-of-the-art methods for Learning to Rank (LtR), either designed for tabular or textual data, are incredibly complex. Increasing the complexity of the models has many drawbacks, including difficulties in understanding the logic behind each prediction and a lack of trust in the system during its deployment. In this paper, which describes the author's goals during his Ph.D., there is an analysis and discussion of how we can use the ideas and tools coming from the eXplainable Artificial Intelligence (XAI) field to make the most effective methods for LtR understandable to the practitioners with the final goal of making them more efficient and/or understand better when they can be improved. The strategies adopted to achieve the aforementioned goals are different and based on the type of models analyzed, which go from more traditional LtR models based on ensembles of decision trees and using handcrafted features to fairly new neural LtR models using text data.
RecRec: Algorithmic Recourse for Recommender Systems
Authors: Sahil Verma, Ashudeep Singh, Varich Boonsanong, John P. Dickerson, Chirag Shah
Venue: CIKM
Year: 2023

Recommender systems play an essential role in the choices people make in domains such as entertainment, shopping, food, news, employment, and education. The machine learning models underlying these recommender systems are often enormously large and black-box in nature for users, content providers, and system developers alike. It is often crucial for all stakeholders to understand the model's rationale behind making certain predictions and recommendations. This is especially true for the content providers whose livelihoods depend on the recommender system. Drawing motivation from the practitioners' need, in this work, we propose a recourse framework for recommender systems, targeted towards the content providers. Algorithmic recourse in the recommendation setting is a set of actions that, if executed, would modify the recommendations (or ranking) of an item in the desired manner. A recourse suggests actions of the form: ''if a feature changes X to Y, then the ranking of that item for a set of users will change to X.'' Furthermore, we demonstrate that RecRec is highly effective in generating valid, sparse, and actionable recourses through an empirical evaluation of recommender systems trained on three real-world datasets. To the best of our knowledge, this work is the first to conceptualize and empirically test a generalized framework for generating recourses for recommender systems.
MArBLE: Hierarchical Multi-Armed Bandits for Human-in-the-Loop Set Expansion
Authors: Muntasir Wahed, Daniel Gruhl, Ismini Lourentzou
Venue: CIKM
Year: 2023

The modern-day research community has an embarrassment of riches regarding pre-trained AI models. Even for a simple task such as lexicon set expansion, where an AI model suggests new entities to add to a predefined seed set of entities, thousands of models are available. However, deciding which model to use for a given set expansion task is non-trivial. In hindsight, some models can be 'off topic' for specific set expansion tasks, while others might work well initially but quickly exhaust what they have to offer. Additionally, certain models may require more careful priming in the form of samples or feedback before being finetuned to the task at hand. In this work, we frame this model selection as a sequential non-stationary problem, where there exist a large number of diverse pre-trained models that may or may not fit a task at hand, and an expert is shown one suggestion at a time to include in the set or not, i.e., accept or reject the suggestion. The goal is to expand the list with the most entities as quickly as possible. We introduce MArBLE, a hierarchical multi-armed bandit method for this task, and two strategies designed to address cold-start problems. Experimental results on three set expansion tasks demonstrate MArBLE's effectiveness compared to baselines.
GraphFADE: Field-aware Decorrelation Neural Network for Graphs with Tabular Features
Authors: Junhong Wan, Yao Fu, Junlan Yu, Weihao Jiang, Shiliang Pu, Ruiheng Yang
Venue: CIKM
Year: 2023

Graph Neural Networks (GNNs) have achieved great success in recent years for their remarkable ability to extract effective representations from both node features and graph structures. Most of GNNs only focus on graphs with homogeneous features that correspond to one single feature field. For tabular features that are heterogeneous with multiple feature fields, GNNs often perform less favorably compared to machine learning methods such as boosted trees. In this work, we propose a new perspective to uncover the problem of GNNs on graphs with tabular features through both empirical study and theoretical analysis. The assumption of GNNs that connected nodes exhibit similar patterns can barely hold true for tabular features since multiple feature fields already exhibit different patterns. And propagation on such mismatched graph causes propagated features overcorrelated on graphs, which leads to the reduction of feature diversity and the increase of information redundancy. Therefore, we propose Field-aware Decorrelation Neural Network for graphs with tabular features (GraphFADE), a novel framework that directly optimizes the overcorrelation problem for graphs with tabular features. We first hierarchically partition the dataset into subsets with minimal correlation and then according to the decorrelation clustering results assemble the optimal matched graphs for each feature dimension to propagate on. The empirical study shows that our method achieves superior performance on multiple graphs with tabular features, demonstrating the effectiveness of our model.
PSLF: Defending Against Label Leakage in Split Learning
Authors: Xinwei Wan, Jiankai Sun, Shengjie Wang, Lei Chen, Zhenzhe Zheng, Fan Wu, Guihai Chen
Venue: CIKM
Year: 2023

With increasing concern over data privacy, split learning has become a widely used distributed machine learning paradigm in practice, where two participants (namely the non-label party and the label party) own raw features and raw labels respectively, and jointly train a model. Although no raw data is communicated between the two parties during model training, several works have demonstrated that data privacy, especially label privacy, is still vulnerable in split learning, and have proposed several defense algorithms against label attacks. However, the theoretical guarantee on the privacy preservation of these algorithms is limited. In this work, we propose a novel Private Split Learning Framework (PSLF). In PSLF, the label party shares only the gradients computed by flipped labels with the non-label party, which improves privacy preservation on raw labels, and meanwhile, we further design an extra sub-model from true labels to improve prediction accuracy. We also design a Flipped Multi-Label Generation mechanism (FMLG) based on randomized response for the label party to generate flipped labels. FMLG is proven differentially private and the label party could make a trade-off between privacy and utility by setting the DP budget. In addition, we design an upsampling method to further protect the labels against some existing attacks. We have evaluated PSLF over real-world datasets to demonstrate its effectiveness in protecting label privacy and achieving promising prediction accuracy.