OpenRaiser/Intern-Atlas huggingface dataset:
Knowledge-Augmented Methods for Natural Language Processing
Authors: Chenguang Zhu, Yichong Xu, Xiang Ren, Bill Yuchen Lin, Meng Jiang, Wenhao Yu
Venue: WSDM
Year: 2023

Knowledge in natural language processing (NLP) has been a rising trend especially after the advent of large scale pre-trained models. NLP models with attention to knowledge can i) access unlimited amount of external information; ii) delegate the task of storing knowledge from its parameter space to knowledge sources; iii) obtain up-to-date information; iv) make prediction results more explainable via selected knowledge. In this tutorial, we will introduce the key steps in integrating knowledge into NLP, including knowledge grounding from text, knowledge representation and fusing. In addition, we will introduce recent state-of-the-art applications in fusing knowledge into language understanding, language generation and commonsense reasoning.
Self-Supervised Graph Structure Refinement for Graph Neural Networks
Authors: Jianan Zhao, Qianlong Wen, Mingxuan Ju, Chuxu Zhang, Yanfang Ye
Venue: WSDM
Year: 2023

Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most existing GSL works apply a joint learning framework where the estimated adjacency matrix and GNN parameters are optimized for downstream tasks. However, as GSL is essentially a link prediction task, whose goal may largely differ from the goal of the downstream task. The inconsistency of these two goals limits the GSL methods to learn the potential optimal graph structure. Moreover, the joint learning framework suffers from scalability issues in terms of time and space during the process of estimation and optimization of the adjacency matrix. To mitigate these issues, we propose a graph structure refinement (GSR) framework with a pretrain-finetune pipeline. Specifically, The pre-training phase aims to comprehensively estimate the underlying graph structure by a multi-view contrastive learning framework with both intra- and inter-view link prediction tasks. Then, the graph structure is refined by adding and removing edges according to the edge probabilities estimated by the pre-trained model. Finally, the fine-tuning GNN is initialized by the pre-trained model and optimized toward downstream tasks. With the refined graph structure remaining static in the fine-tuning space, GSR avoids estimating and optimizing graph structure in the fine-tuning phase which enjoys great scalability and efficiency. Moreover, the fine-tuning GNN is boosted by both migrating knowledge and refining graphs. Extensive experiments are conducted to evaluate the effectiveness (best performance on six benchmark datasets), efficiency, and scalability (13.8 times faster using 32.8% GPU memory compared to the best GSL baseline on Cora) of the proposed model.
AgAsk: A Conversational Search Agent for Answering Agricultural Questions
Authors: Hang Li, Bevan Koopman, Ahmed Mourad, Guido Zuccon
Venue: WSDM
Year: 2023

While large amounts of potentially useful agricultural resources (journal articles, manuals, reports) are available, their value cannot be realised if they cannot be easily searched and presented to the agriculture users in a digestible form.AgAsk is a conversational search system for the agricultural domain, providing tailored answers to growers questions. AgAsk is underpinned by an efficient and effective neural passage ranking model fine-tuned on real world growers' questions. An adaptable, messaging-style user interface is deployed via the Telegram messaging platform, allowing users to ask natural language questions via text or voice, and receive short natural language answers as replies. AgAsk is empirically evaluated on an agricultural passage retrieval test collection. The system provides a single entry point to access the information needed for better growing decisions. Much of the system is domain agnostic and would benefit other domains. AgAsk can be interacted via Telegram; further information about AgAsk, including codebases, instructions and demonstration videos can be accessed at https://ielab.io/publications/agask-agent.
Calibrated Recommendations as a Minimum-Cost Flow Problem
Authors: Himan Abdollahpouri, Zahra Nazari, Alex Gain, Clay Gibson, Maria Dimakopoulou, Jesse Anderton, Benjamin A. Carterette, Mounia Lalmas, Tony Jebara
Venue: WSDM
Year: 2023

Calibration in recommender systems has recently gained significant attention. In the recommended list of items, calibration ensures that the various (past) areas of interest of a user are reflected with their corresponding proportions. For instance, if a user has watched, say, 80 romance movies and 20 action movies, then it is reasonable to expect the recommended list of movies to be comprised of about 80% romance and 20% action movies as well. Calibration is particularly important given that optimizing towards accuracy often leads to the user's minority interests being dominated by their main interests, or by a few overall popular items, in the recommendations they receive. In this paper, we propose a novel approach based on the max flow problem for generating calibrated recommendations. In a series of experiments using two publicly available datasets, we demonstrate the superior performance of our proposed approach compared to the state-of-the-art in generating relevant and calibrated recommendation lists.
UserSimCRS: A User Simulation Toolkit for Evaluating Conversational Recommender Systems
Authors: Jafar Afzali, Aleksander Mark Drzewiecki, Krisztian Balog, Shuo Zhang
Venue: WSDM
Year: 2023

We present an extensible user simulation toolkit to facilitate automatic evaluation of conversational recommender systems. It builds on an established agenda-based approach and extends it with several novel elements, including user satisfaction prediction, persona and context modeling, and conditional natural language generation. We showcase the toolkit with a pre-existing movie recommender system and demonstrate its ability to simulate dialogues that mimic real conversations, while requiring only a handful of manually annotated dialogues as training data.
Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data
Authors: M. Eren Akbiyik, Mert Erkul, Killian Kämpf, Vaiva Vasiliauskaite, Nino Antulov-Fantulin
Venue: WSDM
Year: 2023

Understanding the variations in trading price (volatility), and its response to exogenous information, is a well-researched topic in finance. In this study, we focus on finding stable and accurate volatility predictors for a relatively new asset class of cryptocurrencies, in particular Bitcoin, using deep learning representations of public social media data obtained from Twitter. For our experiments, we extracted semantic information and user statistics from over 30 million Bitcoin-related tweets, in conjunction with 15-minute frequency price data over a horizon of 144 days. Using this data, we built several deep learning architectures that utilized different combinations of the gathered information. For each model, we conducted ablation studies to assess the influence of different components and feature sets over the prediction accuracy. We found statistical evidences for the hypotheses that: (i) temporal convolutional networks perform significantly better than both classical autoregressive models and other deep learning-based architectures in the literature, and (ii) tweet author meta-information, even detached from the tweet itself, is a better predictor of volatility than the semantic content and tweet volume statistics. We demonstrate how different information sets gathered from social media can be utilized in different architectures and how they affect the prediction results. As an additional contribution, we make our dataset public for future research.
Travel Bird: A Personalized Destination Recommender with TourBERT and Airbnb Experiences
Authors: Veronika Arefieva, Roman Egger, Michael Schrefl, Markus Schedl
Venue: WSDM
Year: 2023

We present Travel Bird, a novel personalized destination recommendation and exploration interface which allows its users to find their next tourist destination by describing their specific preferences in a narrative form. Unlike other solutions, Travel Bird is based on TourBERT, a novel NLP model we developed, specifically tailored to the tourism domain. Travel Bird creates a two-dimensional personalized destination exploration space from TourBERT embeddings of social media content and the users' textual description of the experience they are looking for. In this demo, we will showcase several use cases for Travel Bird, which are beneficial for consumers and destination management organizations.
A Personalized Neighborhood-based Model for Within-basket Recommendation in Grocery Shopping
Authors: Mozhdeh Ariannezhad, Ming Li, Sebastian Schelter, Maarten de Rijke
Venue: WSDM
Year: 2023

Users of online shopping platforms typically purchase multiple items at a time in the form of a shopping basket. Personalized within-basket recommendation is the task of recommending items to complete an incomplete basket during a shopping session. In contrast to the related task of session-based recommendation, where the goal is to complete an ongoing anonymous session, we have access to the shopping history of the user in within-basket recommendation. Previous studies have shown the superiority of neighborhood-based models for session-based recommendation and the importance of personal history in the grocery shopping domain. But their applicability in within-basket recommendation remains unexplored. We propose PerNIR, a neighborhood-based model that explicitly models the personal history of users for within-basket recommendation in grocery shopping. The main novelty of PerNIR is in modeling the short-term interests of users, which are represented by the current basket, as well as their long-term interest, which is reflected in their purchasing history. In addition to the personal history, user neighbors are used to capture the collaborative purchase behavior. We evaluate PerNIR on two public and proprietary datasets. The experimental results show that it outperforms 10 state-of-the-art competitors with a significant margin, i.e., with gains of more than 12% in terms of hit rate over the second best performing approach. Additionally, we showcase an optimized implementation of our method, which computes recommendations fast enough for real-world production scenarios.
UnCommonSense in Action! Informative Negations for Commonsense Knowledge Bases
Authors: Hiba Arnaout, Tuan-Phong Nguyen, Simon Razniewski, Gerhard Weikum
Venue: WSDM
Year: 2023

Knowledge bases about commonsense knowledge i.e., CSKBs, are crucial in applications such as search and question answering. Prominent CSKBs mostly focus on positive statements. In this paper we show that materializing important negations increases the usability of CSKBs. We present Uncommonsense, a web portal to explore informative negations about everyday concepts: (i) in a research-focused interface, users get a glimpse into results-per-steps of the methodology; (ii) in a trivia interface, users can browse fun negative trivia about concepts of their choice; and (iii) in a query interface, users can submit triple-pattern queries with explicit negated relations and compare results with significantly less relevant answers from the positive-only baseline. It can be accessed at:https://uncommonsense.mpi-inf.mpg.de/.
Simulating Humans at Scale to Evaluate Voice Interfaces for TVs: the Round-Trip System at Comcast
Authors: Breck Baldwin, Lauren Reese, Liming Zhang, Jan Neumann, Taylor Cassidy, Michael Pereira, G. Craig Murray, Kishorekumar Sundararajan, Yidnekachew Endale, Pramod Kadagattor, Paul Wolfe, Brian Aiken, Tony Braskich, Donte Jiggetts, Adam Sloan, Esther Vaturi, Crystal Pender, Ferhan Ture
Venue: WSDM
Year: 2023

Evaluating large-scale customer-facing voice interfaces involves a variety of challenges, such as data privacy, fairness or unintended bias, and the cost of human labor. Comcast's Xfinity Voice Remote is one such voice interface aimed at users looking to discover content on their TVs. The artificial intelligence (AI) behind the voice remote currently powers multiple voice interfaces, serving tens of millions of requests every day, from users across the globe.In this talk, we introduce a novel Round-Trip system we have built to evaluate the AI serving these voice interfaces in a semi-automated manner, providing a robust and cheap alternative to traditional quality assurance methods. We discuss five specific challenges we have encountered in Round-Trip and describe our solutions in detail.
Beyond Hard Negatives in Product Search: Semantic Matching Using One-Class Classification (SMOCC)
Authors: Arindam Bhattacharya, Ankit Gandhi, Vijay Huddar, Ankith M. S, Aayush Moroney, Atul Saroop, Rahul Bhagat
Venue: WSDM
Year: 2023

Semantic matching is an important component of a product search pipeline. Its goal is to capture the semantic intent of the search query as opposed to the syntactic matching performed by a lexical matching system. A semantic matching model captures relationships like synonyms, and also captures common behavioral patterns to retrieve relevant results by generalizing from purchase data. They however suffer from lack of availability of informative negative examples for model training. Various methods have been proposed in the past to address this issue based upon hard-negative mining and contrastive learning. In this work, we propose a novel method for semantic matching based on one-class classification called SMOCC. Given a query and a relevant product, SMOCC generates the representation of an informative negative which is then used to train the model. Our method is based on the idea of generating negatives by using adversarial search in the neighborhood of the positive examples. We also propose a novel approach for selecting the radius to generate adversarial negative products around queries based on the model's understanding of the query. Depending on how we select the radius, we propose two variants of our method: SMOCC-QS, that quantizes the queries using their specificity, and SMOCC-EM, that uses expectation-maximization paradigm to iteratively learn the best radius. We show that our method outperforms the state-of-the-art hard negative mining approaches by increasing the purchase recall by 3 percentage points, and improving the percentage of exacts retrieved by up to 5 percentage points while reducing irrelevant results by 1.8 percentage points.
MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature Distribution
Authors: Wendong Bi, Lun Du, Qiang Fu, Yanlin Wang, Shi Han, Dongmei Zhang
Venue: WSDM
Year: 2023

Graph Neural Networks (GNNs) have shown expressive performance on graph representation learning by aggregating information from neighbors. Recently, some studies have discussed the importance of modeling neighborhood distribution on the graph. However, most existing GNNs aggregate neighbors' features through single statistic (e.g., mean, max, sum), which loses the information related to neighbor's feature distribution and therefore degrades the model performance. In this paper, inspired by the method of moment in statistical theory, we propose to model neighbor's feature distribution with multi-order moments. We design a novel GNN model, namely Mix-Moment Graph Neural Network (MM-GNN), which includes a Multi-order Moment Embedding (MME) module and an Element-wise Attention-based Moment Adaptor module. MM-GNN first calculates the multi-order moments of the neighbors for each node as signatures, and then use an Element-wise Attention-based Moment Adaptor to assign larger weights to important moments for each node and update node representations. We conduct extensive experiments on 15 real-world graphs (including social networks, citation networks and web-page networks etc.) to evaluate our model, and the results demonstrate the superiority of MM-GNN over existing state-of-the-art models.
Cognition-aware Knowledge Graph Reasoning for Explainable Recommendation
Authors: Qingyu Bing, Qiannan Zhu, Zhicheng Dou
Venue: WSDM
Year: 2023

Knowledge graphs (KGs) have been widely used in recommendation systems to improve recommendation accuracy and interpretability effectively. Recent research usually endows KG reasoning to find the multi-hop user-item connection paths for explaining why an item is recommended. The existing path-finding process is well designed by logic-driven inference algorithms, while there exists a gap between how algorithms and users perceive the reasoning process. Factually, human thinking is a natural reasoning process that can provide more proper and convincing explanations of why particular decisions are made. Motivated by the Dual Process Theory in cognitive science, we propose a cognition-aware KG reasoning model CogER for Explainable Recommendation, which imitates the human cognition process and designs two modules, i.e., System~1 (making intuitive judgment) and System~2 (conducting explicit reasoning), to generate the actual decision-making process. At each step during the cognition-aware reasoning process, System~1 generates an intuitive estimation of the next-step entity based on the user's historical behavior, and System~2 conducts explicit reasoning and selects the most promising knowledge entities. These two modules work iteratively and are mutually complementary, enabling our model to yield high-quality recommendations and proper reasoning paths. Experiments on three real-world datasets show that our model achieves better recommendation results with explanations compared with previous methods.
SmartCityBus - A Platform for Smart Transportation Systems
Authors: Georgios Bouloukakis, Chrysostomos Zeginis, Nikolaos Papadakis, Kostas Magoutis, George Christodoulou, Chrysanthi Kosyfaki, Konstantinos Lampropoulos, Nikos Mamoulis
Venue: WSDM
Year: 2023

With the growth of the Internet of Things (IoT), Smart(er) Cities have been a research goal of researchers, businesses and local authorities willing to adopt IoT technologies to improve their services. Among them, Smart Transportation [7,8], the integrated application of modern technologies and management strategies in transportation systems, refers to the adoption of new IoT solutions to improve urban mobility. These technologies aim to provide innovative solutions related to different modes of transport and traffic management and enable users to be better informed and make safer and 'smarter' use of transport networks. This talk presents SmartCityBus, a data-driven intelligent transportation system (ITS) whose main objective is to use online and offline data in order to provide accurate statistics and predictions and improve public transportation services in the short and medium/long term.
Towards Universal Cross-Domain Recommendation
Authors: Jiangxia Cao, Shaoshuai Li, Bowen Yu, Xiaobo Guo, Tingwen Liu, Bin Wang
Venue: WSDM
Year: 2023

In industry, web platforms such as Alibaba and Amazon often provide diverse services for users. Unsurprisingly, some developed services are data-rich, while some newly started services are data-scarce accompanied by severe data sparsity and cold-start problems. To alleviate the above problems and incubate new services easily, cross-domain recommendation (CDR) has attracted much attention from industrial and academic researchers. Generally, CDR aims to transfer rich user-item interaction information from related source domains (e.g., developed services) to boost recommendation quality of target domains (e.g., newly started services). For different scenarios, previous CDR methods can be roughly divided into two branches: (1) Data sparsity CDR fulfills user preference aided by other domain data to make intra-domain recommendations for users with few interactions, (2) Cold-start CDR projects user preference from other domain to make inter-domain recommendations for users with none interactions. In the past years, many outstanding CDR methods are emerged, however, to the best of our knowledge, none of them attempts to solve the two branches simultaneously. In this paper, we provide a unified framework, namely UniCDR, which can universally model different CDR scenarios by transferring the domain-shared information. Extensive experiments under the above 2 branches on 4 CDR scenarios and 6 public and large-scale industrial datasets demonstrate the effectiveness and universal ability of our UniCDR.
Learning and Maximizing Influence in Social Networks Under Capacity Constraints
Authors: Pritish Chakraborty, Sayan Ranu, Krishna Sri Ipsit Mantri, Abir De
Venue: WSDM
Year: 2023

Influence maximization (IM) refers to the problem of finding a subset of nodes in a network through which we could maximize our reach to other nodes in the network. This set is often called the "seed set", and its constituent nodes maximize the social diffusion process. IM has previously been studied in various settings, including under a time deadline, subject to constraints such as that of budget or coverage, and even subject to measures other than the centrality of nodes. The solution approach has generally been to prove that the objective function is submodular, or has a submodular proxy, and thus has a close greedy approximation. In this paper, we explore a variant of the IM problem where we wish to reach out to and maximize the probability of infection of a small subset of bounded capacity K. We show that this problem does not exhibit the same submodular guarantees as the original IM problem, for which we resort to the theory of gamma-weakly submodular functions. Subsequently, we develop a greedy algorithm that maximizes our objective despite the lack of submodularity. We also develop a suitable learning model that out-competes baselines on the task of predicting the top-K infected nodes, given a seed set as input.
Adversarial Autoencoder for Unsupervised Time Series Anomaly Detection and Interpretation
Authors: Xuanhao Chen, Liwei Deng, Yan Zhao, Kai Zheng
Venue: WSDM
Year: 2023

In many complex systems, devices are typically monitored and generating massive multivariate time series. However, due to the complex patterns and little useful labeled data, it is a great challenge to detect anomalies from these time series data. Existing methods either rely on less regularizations, or require a large number of labeled data, leading to poor accuracy in anomaly detection. To overcome the limitations, in this paper, we propose an adversarial autoencoder anomaly detection and interpretation framework named DAEMON, which performs robustly for various datasets. The key idea is to use two discriminators to adversarially train an autoencoder to learn the normal pattern of multivariate time series, and thereafter use the reconstruction error to detect anomalies. The robustness of DAEMON is guaranteed by the regularization of hidden variables and reconstructed data using the adversarial generation method. An unsupervised approach used to detect anomalies is proposed. Moreover, in order to help operators better diagnose anomalies, DAEMON provides anomaly interpretation by computing the gradients of anomalous data. An extensive empirical study on real data offers evidence that the framework is capable of outperforming state-of-the-art methods in terms of the overall F1-score and interpretation accuracy for time series anomaly detection.