OpenRaiser/Intern-Atlas huggingface dataset:
Locality Sensitive Hashing with Temporal and Spatial Constraints for Efficient Population Record Linkage
Authors: Charini Nanayakkara, Peter Christen
Venue: CIKM
Year: 2022

Record linkage is the process of identifying which records within or across databases refer to the same entity. Min-hash based Locality Sensitive Hashing (LSH) is commonly used in record linkage as a blocking technique to reduce the number of records to be compared. However, when applied on large databases, min-hash LSH can yield highly skewed block size distributions and many redundant record pair comparisons, where only few of those correspond to true matches (records that refer to the same entity). Furthermore, min-hash LSH is highly parameter sensitive and requires trial and error to determine the optimal trade-off between blocking quality and efficiency of the record pair comparison step. In this paper, we present a novel method to improve the scalability and robustness of min-hash LSH for linking large population databases by exploiting temporal and spatial information available in personal data, and by filtering record pairs based on block sizes and min-hash similarity. Our evaluation on three real-world data sets shows that our method can improve the efficiency of record pair comparison by 75% to 99%, whereas the final average linkage precision can be improved by 28% at the cost of a reduction in the average recall by 4%.
Domain-Agnostic Contrastive Representations for Learning from Label Proportions
Authors: Jay Nandy, Rishi Saket, Prateek Jain, Jatin Chauhan, Balaraman Ravindran, Aravindan Raghuveer
Venue: CIKM
Year: 2022

We study the weak supervision learning problem of Learning from Label Proportions (LLP) where the goal is to learn an instance-level classifier using proportions of various class labels in a bag -- a collection of input instances that often can be highly correlated. While representation learning for weakly-supervised tasks is found to be effective, they often require domain knowledge. To the best of our knowledge, representation learning for tabular data (unstructured data containing both continuous and categorical features) are not studied. In this paper, we propose to learn diverse representations of instances within the same bags to effectively utilize the weak bag-level supervision. We propose a domain agnostic LLP method, called "Self Contrastive Representation Learning for LLP" (SelfCLR-LLP) that incorporates a novel self--contrastive function as an auxiliary loss to learn representations on tabular data for LLP. We show that diverse representations for instances within the same bags aid efficient usage of the weak bag-level LLP supervision. We evaluate the proposed method through extensive experiments on real-world LLP datasets from e-commerce applications to demonstrate the effectiveness of our proposed SelfCLR-LLP. In this paper, we propose to learn diverse representations of instances within the same bags to effectively utilize the weak bag-level supervision. We propose a domain agnostic LLP method, called "Self Contrastive Representation Learning for LLP" (SelfCLR-LLP) that incorporates a novel self--contrastive function as an auxiliary loss to learn representations on tabular data for LLP. We show that diverse representations for instances within the same bags aid efficient usage of the weak bag-level LLP supervision. We evaluate the proposed method through extensive experiments on real-world LLP datasets from e-commerce applications to demonstrate the effectiveness of our proposed SelfCLR-LLP.
Sensitivity Review of Large Collections by Identifying and Prioritising Coherent Documents Groups
Authors: Hitarth Narvala, Graham McDonald, Iadh Ounis
Venue: CIKM
Year: 2022

With the massive increase in the volume of digitally produced documents, government departments face a logistical issue when conducting the manual sensitivity review of documents that should be opened to the public. When reviewing a document, sensitivity reviewers often need to quickly access related information from other documents in the collection. For example, documents that mention the same topic or event can provide the reviewers with useful contextual information and assist the reviewers to make consistent sensitivity judgements more quickly. However, it is infeasible to manually identify groups of such related documents in large unstructured collections. In this work, we present a sensitivity review system that automatically identifies groups of related documents to assist reviewers and increase the efficiency of sensitivity review. In particular, our system groups the documents that are to be sensitivity reviewed based on the documents' semantic categories (e.g., criminality). Moreover, the system identifies chronological and coherent information threads to describe the full context of an event, activity or discussion that may be spread across multiple documents. Additionally, the system prioritises the identified semantic categories and information threads for review by leveraging automatic sensitivity classification to maximise the number of documents that can be opened to the public in a limited reviewing time-budget.
Rationale Aware Contrastive Learning Based Approach to Classify and Summarize Crisis-Related Microblogs
Authors: Thi Huyen Nguyen, Koustav Rudra
Venue: CIKM
Year: 2022

Recent fashion of information propagation on Twitter makes the platform a crucial conduit for tactical data and emergency responses during disasters. However, the real-time information about crises is immersed in a large volume of emotional and irrelevant posts. It brings the necessity to develop an automatic tool to identify disaster-related messages and summarize the information for data consumption and situation planning. Besides, explainability of the methods is crucial in determining their applicability in real-life scenarios. Recent studies also highlight the importance of learning a good latent representation of tweets for several downstream tasks. In this paper, we take advantage of state-of-the-art methods, such as transformers and contrastive learning to build an interpretable classifier. Our proposed model classifies Twitter messages into different humanitarian categories and also extracts rationale snippets as supporting evidence for output decisions. The contrastive learning framework helps to learn better representations of tweets by bringing the related tweets closer in the embedding space. Furthermore, we employ classification labels and rationales to efficiently generate summaries of crisis events. Extensive experiments over different crisis datasets show that (i). our classifier obtains the best performance-interpretability trade-off, (ii). the proposed summarizer shows superior performance (1.4%-22% improvement) with significantly less computation cost than baseline models.
PyDHNet: A Python Library for Dynamic Heterogeneous Network Representation Learning and Evaluation
Authors: Hoang Nguyen, Radin Hamidi Rad, Ebrahim Bagheri
Venue: CIKM
Year: 2022

Network representation learning and its applications have received increasing attention. Due to their various application areas, many research groups have developed a diverse range of software tools and techniques to learn representation for different types of networks. However, to the best of our knowledge, there are limited works that support representation learning for dynamic heterogeneous networks. The work presented in this demonstration paper attempts to fill the gap in this space by developing and publicly releasing an open-source Python library known as, PyDHNet, a Python Library for Dynamic Heterogeneous Network Representation Learning and Evaluation. PyDHNet consists of two main components: dynamic heterogeneous network representation learning and task-specific evaluation. In our paper, we demonstrate that PyDHNet has an extensible architecture, is easy to install (through PIP) and use, and integrates quite seamlessly with other Python libraries. We also show that the implementation for PyDHNet is efficient and enjoys a competitive execution time.
CrisICSum: Interpretable Classification and Summarization Platform for Crisis Events from Microblogs
Authors: Thi Huyen Nguyen, Miroslav Shaltev, Koustav Rudra
Venue: CIKM
Year: 2022

Microblogging platforms such as Twitter, receive massive messages during crisis events. Real-time insights are crucial for emergency response. Hence, there is a need to develop faithful tools for efficiently digesting information. In this paper, we present CrisICSum, a platform for classification and summarization of crisis events. The objective of CrisICSum is to classify user posts during disaster events into different humanitarian classes (i.e., damage, affected people, etc.) and generate summaries of class-level messages. Unlike existing systems, CrisICSum employs an interpretable by design backend classifier. It can generate explanations for output decisions. Besides, the platform allows user feedback on both classification and summarization phases. CrisICSum is designed and run as an easily integrated web application. Backend models are interchangeable. The system can assist users and human organizations in improving response efforts during disaster situations. CrisICSum is available at https://crisicsum.l3s.uni-hannover.de
Leveraging Automated Search Relevance Evaluation to Improve System Deployment: A Case Study in Healthcare
Authors: Yizhao Ni, Ferosh Jacob, Priya Gopi Achuthan, Hui Wu, Faizan Javed
Venue: CIKM
Year: 2022

Over the last year, a digital initiative has been focused on reengineering the search engine for kp.org, a health web portal serving over 12 million members. However, traditional software testing techniques that rely on limited use cases and consistent behavior are neither comprehensive nor specific for capturing complex user search behaviors. To support system deployment, we utilize information retrieval (IR) technologies to monitor search performance, identify areas of improvement and suggest actionable items. In this case study we share industrial experience on building an IR evaluation pipeline and its usage to inform deployment and improve system development. The work emphasizes domain specific challenges, best practices and lessons learned during system deployment in a healthcare setting. It features the ability of IR techniques to strengthen collaboration between data scientists, software engineers and product managers in making data-driven decisions.
MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization
Authors: Hui Niu, Siyuan Li, Jian Li
Venue: CIKM
Year: 2022

Portfolio management is a fundamental problem in finance. It involves periodic reallocations of assets to maximize the expected returns within an appropriate level of risk exposure. Deep reinforcement learning (RL) has been considered a promising approach to solving this problem owing to its strong capability in sequential decision making. However, due to the non-stationary nature of financial markets, applying RL techniques to portfolio optimization remains a challenging problem. Extracting trading knowledge from various expert strategies could be helpful for agents to accommodate the changing markets. In this paper, we propose MetaTrader, a novel two-stage RL-based approach for portfolio management, which learns to integrate diverse trading policies to adapt to various market conditions. In the first stage, MetaTrader incorporates an imitation learning objective into the reinforcement learning framework. Through imitating different expert demonstrations, MetaTrader acquires a set of trading policies with great diversity. In the second stage, MetaTrader learns a meta-policy to recognize the market conditions and decide on the most proper learned policy to follow. We evaluate the proposed approach on three real-world index datasets and compare it to state-of-the-art baselines. The empirical results demonstrate that MetaTrader significantly outperforms those baselines in balancing profits and risks. Furthermore, thorough ablation studies validate the effectiveness of the components in the proposed approach.
Implicit Session Contexts for Next-Item Recommendations
Authors: Sejoon Oh, Ankur Bhardwaj, Jongseok Han, Sungchul Kim, Ryan A. Rossi, Srijan Kumar
Venue: CIKM
Year: 2022

\noindent Session-based recommender systems capture the short-term interest of a user within a session. Session contexts (i.e., a user's high-level interests or intents within a session) are not explicitly given in most datasets, and implicitly inferring session context as an aggregation of item-level attributes is crude. In this paper, we propose \method, which implicitly contextualizes sessions. \method first generates implicit contexts for sessions by creating a session-item graph, learning graph embeddings, and clustering to assign sessions to contexts. \method then trains a session context predictor and uses the predicted contexts' embeddings to enhance the next-item prediction accuracy. Experiments on four datasets show that \method has superior next-item prediction accuracy than state-of-the-art models. A case study of \method on the Reddit dataset confirms that assigned session contexts are unique and meaningful.
Asymmetrical Context-aware Modulation for Collaborative Filtering Recommendation
Authors: Yi Ouyang, Peng Wu, Li Pan
Venue: CIKM
Year: 2022

Modern learnable collaborative filtering recommendation models generate user and item representations by deep learning methods (e.g. graph neural networks) for modeling user-item interactions. However, most of them may still have unsatisfied performances due to two issues. Firstly, some models assume that the representations of users or items are fixed when modeling interactions with different objects. However, a user may have different interests in different items, and an item may also have different attractions to different users. Thus the representations of users and items should depend on their contexts to some extent. Secondly, existing models learn representations for user and item by symmetrical dual methods which have identical or similar operations. Symmetrical methods may fail to sufficiently and reasonably extract the features of user and item as their interaction data have diverse semantic properties. To address the above issues, a novel model called Asymmetrical context-awaRe modulation for collaBorative filtering REcommendation (ARBRE) is proposed. It adopts simplified GNNs on collaborative graphs to capture homogeneous user preferences and item attributes, then designs two asymmetrical context-aware modulation models to learn dynamic user interests and item attractions, respectively. The learned representations from user domain and item domain are input pair-wisely into 4 Multi-Layer Perceptrons in different combinations to model user-item interactions. Experimental results on three real-world datasets demonstrate the superiority of ARBRE over various state-of-the-arts.
Identify Relevant Entities Through Text Understanding
Authors: Pooja Oza
Venue: CIKM
Year: 2022

An Entity Retrieval system is a fundamental task of Information Retrieval that provides direct answer to an information need of user. Prior work of entity retrieval utilizes either the Knowledge Graph fields or the text relevant to the query via pseudo-relevance feedback to improve the performance. Recently, Knowledge Graph embeddings or other entity representations, which capture the entity information from a knowledge graph are shown to be beneficial for entity retrieval. However, such embeddings are query-agnostic. In this dissertation work, we aim to improve entity retrieval by exploring the pseudo-relevance feedback to generate entity representations that capture query-aware entity information to determine the relevance of entities. We study the effectiveness of pseudo-relevance feedback against Knowledge Graph fields and investigate the efficacy of the Knowledge Graph embeddings for entity retrieval. We aim to understand the importance of utilization of query-aware signals and modeling of such signals with Knowledge Graph embeddings. Our results show that pseudo-relevance feedback is more effective than the Knowledge Graph fields by 30%.
Sequence Prediction under Missing Data: An RNN Approach without Imputation
Authors: Soumen Pachal, Avinash Achar
Venue: CIKM
Year: 2022

Missing data scenarios are very common in ML applications in general and time-series/sequence applications are no exceptions. This paper pertains to a novel Recurrent Neural Network (RNN) based solution for sequence prediction under missing data. Our method is distinct from all existing approaches. It tries to encode the missingness patterns in the data directly without trying to impute data either before or during model building. Our encoding is lossless and achieves compression. It can be employed for both sequence classification and forecasting. We focus on forecasting here in a general context of multi-step prediction in presence of possible exogenous inputs. In particular, we propose novel variants of Encoder-Decoder (Seq2Seq) RNNs for this. The encoder here adopts the above mentioned pattern encoding, while at the decoder which has a different structure, multiple variants are feasible. We demonstrate the utility of our proposed architecture via multiple experiments on both single and multiple sequence (real) data-sets. We consider both scenarios where (i)data is naturally missing and (ii)data is synthetically masked.
Multimodal Meta-Learning for Cold-Start Sequential Recommendation
Authors: Xingyu Pan, Yushuo Chen, Changxin Tian, Zihan Lin, Jinpeng Wang, He Hu, Wayne Xin Zhao
Venue: CIKM
Year: 2022

In this paper, we study the task of cold-start sequential recommendation, where new users with very short interaction sequences come with time. We cast this problem as a few-shot learning problem and adopt a meta-learning approach to developing our solution. For our task, a major obstacle of effective knowledge transfer that is there exists significant characteristic divergence between old and new interaction sequences for meta-learning. To address the above issues, we purpose a Multimodal MetaLearning (denoted as MML) approach that incorporates multimodal side information of items (e.g., text and image) into the meta-learning process, to stabilize and improve the meta-learning process for cold-start sequential recommendation. In specific, we design a group of multimodal meta-learners corresponding to each kind of modality, where ID features are used to develop the main meta-learner and the rest text and image features are used to develop auxiliary meta-learners. Instead of simply combing the predictions from different meta-learners, we design an adaptive, learnable fusion layer to integrate the predictions based on different modalities. Meanwhile, we design a cold-start item embedding generator, which utilize multimodal side information to warm up the ID embeddings of new items. Extensive offline and online experiments demonstrate that MML can significantly improve the recommendation performance for cold-start users compared with baseline models. Our code is released at https://github.com/RUCAIBox/MML.
Analysis of Knowledge Transfer in Kernel Regime
Authors: Ashkan Panahi, Arman Rahbar, Chiranjib Bhattacharyya, Devdatt P. Dubhashi, Morteza Haghir Chehreghani
Venue: CIKM
Year: 2022

Knowledge transfer is shown to be a very successful technique for training neural classifiers: together with the ground truth data, it uses the "privileged information" (PI) obtained by a "teacher" network to train a "student" network. It has been observed that classifiers learn much faster and more reliably via knowledge transfer. However, there has been little or no theoretical analysis of this phenomenon. To bridge this gap, we propose to approach the problem of knowledge transfer by regularizing the fit between the teacher and the student with PI provided by the teacher. Using tools from dynamical systems theory, we show that when the student is an extremely wide two layer network, we can analyze it in the kernel regime and show that it is able to interpolate between PI and the given data. This characterization sheds new light on the relation between the training error and capacity of the student relative to the teacher. Another contribution of the paper is a quantitative statement on the convergence of student network. We prove that the teacher reduces the number of required iterations for a student to learn, and consequently improves the generalization power of the student. We give corresponding experimental analysis that validates the theoretical results and yield additional insights.
Learning-to-Spell: Weak Supervision based Query Correction in E-Commerce Search with Small Strong Labels
Authors: Madhura Pande, Vishal Kakkar, Manish Bansal, Surender Kumar, Chinmay Sharma, Himanshu Malhotra, Praneet Mehta
Venue: CIKM
Year: 2022

For an E-commerce search engine, users finding the right product critically depend on spell correction. A misspelled query can fetch totally unrelated results which in turn leads to a bad customer experience. Around 32% of queries have spelling mistakes on our e-commerce search engine. The spell problem becomes more challenging when most spell errors arise from customers with little or no exposure to the English language besides the usual source of accidental mistyping on keyboard. These spell errors are heavily influenced by the colloquial and spoken accents of the customers. This limits the benefit from using generic spell correction systems which are learnt from cleaner English sources like Brown Corpus and Wikipedia with a very low focus on phonetic/vernacular spell errors. In this work, we present a novel approach towards spell correction that effectively solves a very diverse set of spell errors and outperforms several state-of-the-art systems in the domain of E-commerce search. Our strategy combines Learning-to-Rank on a small strongly labelled data with multiple learners trained with weakly labelled data. We report the effectiveness of our solution WellSpell (Weak and strong Labels for Learning to Spell) with both the offline evaluations and online A/B experiment.
Applied Machine Learning Methods for Time Series Forecasting
Authors: Linsey Pang, Wei Liu, Lingfei Wu, Kexin Xie, Stephen D. Guo, Raghav Chalapathy, Musen Wen
Venue: CIKM
Year: 2022

Time series data is ubiquitous, and accurate time series forecasting is vital for many real-world application domains, including retail, healthcare, supply chain, climate science, e-commerce and economics. Forecasting, in general, has led to broad impact and a diverse range of applications. However, with large-scale, high-dimensional time-series data available, more advanced techniques must be invented or improved for highly accurate predictions. Latest data mining and machine learning techniques play a crucial role in the next generation of forecasting models. In this Applied Machine Learning Methods for Time Series Forecasting (AMLTS) workshop, we focus on effective and accurate latest machine learning approaches to solve various real-world problems. With this workshop's ability to attract audiences across various domains, we invite experienced industrial practitioners and researchers to help uncover new approaches and break new ground in time-series modelings' challenging and vital settings.