OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting
Authors: Liangzhe Han, Bowen Du, Leilei Sun, Yanjie Fu, Yisheng Lv, Hui Xiong
Venue: Conference KDD 2021
Year: 2021

Dynamic Graph Neural Networks (DGNNs) have become one of the most promising methods for traffic speed forecasting. However, when adapting DGNNs for traffic speed forecasting, existing approaches are usually built on a static adjacency matrix (no matter predefined or self-learned) to learn spatial relationships among different road segments, even if the impact of two road segments can be changeable dynamically during a day. Moreover, the future traffic speed cannot only be related with the current traffic speed, but also be affected by other factors such as traffic volumes. To this end, in this paper, we aim to explore these dynamic and multi-faceted spatio-temporal characteristics inherent in traffic data for further unleashing the power of DGNNs for better traffic speed forecasting. Specifically, we design a dynamic graph construction method to learn the time-specific spatial dependencies of road segments. Then, a dynamic graph convolution module is proposed to aggregate hidden states of neighbor nodes to focal nodes by message passing on the dynamic adjacency matrices. Moreover, a multi-faceted fusion module is provided to incorporate the auxiliary hidden states learned from traffic volumes with the primary hidden states learned from traffic speeds. Finally, experimental results on real-world data demonstrate that our method can not only achieve the state-of-the-art prediction performances, but also obtain the explicit and interpretable dynamic spatial relationships of road segments.
Modeling Temporal-Spatial Correlations for Crime Prediction
Authors: Xiangyu Zhao, Jiliang Tang
Venue: Conference CIKM 2017
Year: 2017

Crime prediction plays a crucial role in improving public security and reducing the financial loss of crimes. The vast majority of traditional algorithms performed the prediction by leveraging demographic data, which could fail to capture the dynamics of crimes in urban. In the era of big data, we have witnessed advanced ways to collect and integrate fine-grained urban, mobile, and public service data that contains various crime-related sources and rich temporal-spatial information. Such information provides better understandings about the dynamics of crimes and has potentials to advance crime prediction. In this paper, we exploit temporal-spatial correlations in urban data for crime prediction. In particular, we validate the existence of temporal-spatial correlations in crime and develop a principled approach to model these correlations into the coherent framework TCP for crime prediction. The experimental results on real-world data demonstrate the effectiveness of the proposed framework. Further experiments have been conducted to understand the importance of temporal-spatial correlations in crime prediction.
Explainable Fake News Detection with Large Language Model via Defense Among Competing Wisdom
Authors: Bo Wang, Jing Ma, Hongzhan Lin, Zhiwei Yang, Ruichao Yang, Yuan Tian, Yi Chang
Venue: Conference WWW 2024
Year: 2024

Most fake news detection methods learn latent feature representations based on neural networks, which makes them black boxes to classify a piece of news without giving any justification. Existing explainable systems generate veracity justifications from investigative journalism, which suffer from debunking delayed and low efficiency. Recent studies simply assume that the justification is equivalent to the majority opinions expressed in the wisdom of crowds. However, the opinions typically contain some inaccurate or biased information since the wisdom of crowds is uncensored. To detect fake news from a sea of diverse, crowded and even competing narratives, in this paper, we propose a novel defense-based explainable fake news detection framework. Specifically, we first propose an evidence extraction module to split the wisdom of crowds into two competing parties and respectively detect salient evidences. To gain concise insights from evidences, we then design a prompt-based module that utilizes a large language model to generate justifications by inferring reasons towards two possible veracities. Finally, we propose a defense-based inference module to determine veracity via modeling the defense among these justifications. Extensive experiments conducted on two real-world benchmarks demonstrate that our proposed method outperforms state-of-the-art baselines in terms of fake news detection and provides high-quality justifications.
Detecting ponzi schemes on ethereum: Towards healthier blockchain technology
Authors: Weili Chen, Zibin Zheng, Jiahui Cui, Edith C. H. Ngai, Peilin Zheng, Yuren Zhou
Venue: Conference WWW 2018
Year: 2018

Blockchain technology becomes increasingly popular. It also attracts scams, for example, Ponzi scheme, a classic fraud, has been found making a notable amount of money on Blockchain, which has a very negative impact. To help dealing with this issue, this paper proposes an approach to detect Ponzi schemes on blockchain by using data mining and machine learning methods. By verifying smart contracts on Ethereum, we first extract features from user accounts and operation codes of the smart contracts and then build a classification model to detect latent Ponzi schemes implemented as smart contracts. The experimental results show that the proposed approach can achieve high accuracy for practical use. More importantly, the approach can be used to detect Ponzi schemes even at the moment of its creation. By using the proposed approach, we estimate that there are more than 400 Ponzi schemes running on Ethereum. Based on these results, we propose to build a uniform platform to evaluate and monitor every created smart contract for early warning of scams.
Error-Bounded Graph Anomaly Loss for GNNs
Authors: Tong Zhao, Chuchen Deng, Kaifeng Yu, Tianwen Jiang, Daheng Wang, Meng Jiang
Venue: Conference CIKM 2020
Year: 2020

Graph neural networks (GNNs) have been widely used to learn node representations from graph data in an unsupervised way for downstream tasks. However, when applied to detect anomalies (e.g., outliers, unexpected density), they deliver unsatisfactory performance as existing loss functions fail. For example, any loss based on random walk (RW) algorithms would no longer work because the assumption that anomalous nodes were close with each other could not hold. Moreover, the nature of class imbalance in anomaly detection tasks brings great challenges to reduce the prediction error. In this work, we propose a novel loss function to train GNNs for anomaly-detectable node representations. It evaluates node similarity using global grouping patterns discovered from graph mining algorithms. It can automatically adjust margins for minority classes based on data distribution. Theoretically, we prove that the prediction error is bounded given the proposed loss function. We empirically investigate the GNN effectiveness of different loss variants based on different algorithms. Experiments on two real-world datasets show that they perform significantly better than RW-based loss for graph anomaly detection.
RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation
Authors: Chenglin Li, Mingjun Zhao, Huanming Zhang, Chenyun Yu, Lei Cheng, Guoqiang Shu, Beibei Kong, Di Niu
Venue: Conference WSDM 2022
Year: 2022

Cross-domain recommendation can help alleviate the data sparsity issue in traditional sequential recommender systems. In this paper, we propose the RecGURU algorithm framework to generate a Generalized User Representation (GUR) incorporating user information across domains in sequential recommendation, even when there is minimum or no common users in the two domains. We propose a self-attentive autoencoder to derive latent user representations, and a domain discriminator, which aims to predict the origin domain of a generated latent representation. We propose a novel adversarial learning method to train the two modules to unify user embeddings generated from different domains into a single global GUR for each user. The learned GUR captures the overall preferences and characteristics of a user and thus can be used to augment the behavior data and improve recommendations in any single domain in which the user is involved. Extensive experiments have been conducted on two public cross-domain recommendation datasets as well as a large dataset collected from real-world applications. The results demonstrate that RecGURU boosts performance and outperforms various state-of-the-art sequential recommendation and cross-domain recommendation methods. The collected data will be released to facilitate future research.
Between Clicks and Satisfaction: Study on Multi-Phase User Preferences and Satisfaction for Online News Reading
Authors: Hongyu Lu, Min Zhang, Shaoping Ma
Venue: Conference SIGIR 2018
Year: 2018

Click signal has been widely used for designing and evaluating interactive information systems, which is taken as the indicator of user preference. However, click signal does not capture post-click user experience. Very commonly, the user first clicked an item and then found it is not what he wanted after reading its content, which shows there is a gap between user click and user actual preference. Previous studies on web search have incorporated other user behaviors, such as dwell time, to reduce the gap. Unfortunately, for other scenarios such as recommendation and online news reading, there still lacks a thorough understanding of the relationship between click and user preference, and the corresponding reasons which are the focus of this work. Based on an in-depth laboratory user study of online news reading scenario in the mobile environment, we show that click signal does not align with user preference. Besides, we find that user preference changes frequently, hence preferences in three phases are proposed: Before-Read Preference, After-Read Preference and Post-task Preference. In addition, the statistic analysis shows that the changes are highly related to news quality and the context of user interactions. Meanwhile, many other user behaviors, like viewport time, dwell time, and read speed, are found reflecting user preference in different phases. Furthermore, with the help of various kinds of user behaviors, news quality, and interaction context, we build an effective model to predict whether the user actually likes the clicked news. Finally, we replace binary click signals of traditional click-based evaluation metrics, like Click-Through Rate, with the predicted item-level preference, and significant improvements are achieved in estimating the user's list-level satisfaction. Our work sheds light on the understanding of user click behaviors and provides a method for better estimating user interest and satisfaction. The proposed model could also be helpful to various recommendation tasks in mobile scenarios.
Unbiased knowledge distillation for recommendation
Authors: Gang Chen, Jiawei Chen, Fuli Feng, Sheng Zhou, Xiangnan He
Venue: Conference WSDM 2023
Year: 2023

As a promising solution for model compression, knowledge distillation (KD) has been applied in recommender systems (RS) to reduce inference latency. Traditional solutions first train a full teacher model from the training data, and then transfer its knowledge (\iesoft labels ) to supervise the learning of a compact student model. However, we find such a standard distillation paradigm would incur serious bias issue --- popular items are more heavily recommended after the distillation. This effect prevents the student model from making accurate and fair recommendations, decreasing the effectiveness of RS. In this work, we identify the origin of the bias in KD --- it roots in the biased soft labels from the teacher, and is further propagated and intensified during the distillation. To rectify this, we propose a new KD method with a stratified distillation strategy. It first partitions items into multiple groups according to their popularity, and then extracts the ranking knowledge within each group to supervise the learning of the student. Our method is simple and teacher-agnostic --- it works on distillation stage without affecting the training of the teacher model. We conduct extensive theoretical and empirical studies to validate the effectiveness of our proposal. We release our code at: https://github.com/chengang95/UnKD.
Matches Made in Heaven: Toolkit and Large-Scale Datasets for Supervised Query Reformulation
Authors: Negar Arabzadeh, Amin Bigdeli, Shirin Seyedsalehi, Morteza Zihayat, Ebrahim Bagheri
Venue: Conference CIKM 2021
Year: 2021

Researchers have already shown that it is possible to improve retrieval effectiveness through the systematic reformulation of users' queries. Traditionally, most query reformulation techniques relied on unsupervised approaches such as query expansion through pseudo-relevance feedback. More recently and with the increasing effectiveness of neural sequence-to-sequence architectures, the problem of query reformulation has been studied as a supervised query translation problem, which learns to rewrite a query into a more effective alternative. While quite effective in practice, such supervised query reformulation methods require a large number of training instances. In this paper, we present three large-scale query reformulation datasets, namely Diamond, Platinum and Gold datasets, based on the queries in the MS MARCO dataset. The Diamond dataset consists of over 188,000 query pairs where the original source query is matched with an alternative query that has a perfect retrieval effectiveness (an average precision of 1). To the best of our knowledge, this is the first set of datasets for supervised query reformulation that offers perfect query reformulations for a large number of queries. The implementation of our fully automated tool, which is based on a transformer architecture, and our three datasets are made publicly available. We also establish a neural query reformulation baseline performance on our datasets by reporting the performance of strong neural query reformulation baselines. It is our belief that our datasets will significantly impact the development of supervised query reformulation methods in the future.
Convsdg: Session data generation for conversational search
Authors: Fengran Mo, Bole Yi, Kelong Mao, Chen Qu, Kaiyu Huang, Jian-Yun Nie
Venue: Conference WWW 2024
Year: 2024

Conversational search provides a more convenient interface for users to search by allowing multi-turn interaction with the search engine. However, the effectiveness of the conversational dense retrieval methods is limited by the scarcity of training data required for their fine-tuning. Thus, generating more training conversational sessions with relevant labels could potentially improve search performance. Based on the promising capabilities of large language models (LLMs) on text generation, we propose ConvSDG, a simple yet effective framework to explore the feasibility of boosting conversational search by using LLM for session data generation. Within this framework, we design dialogue/session-level and query-level data generation with unsupervised and semi-supervised learning, according to the availability of relevance judgments. The generated data are used to fine-tune the conversational dense retriever. Extensive experiments on four widely used datasets demonstrate the effectiveness and broad applicability of our ConvSDG framework compared with several strong baselines.
How to leverage personal textual knowledge for personalized conversational information retrieval
Authors: Fengran Mo, Longxiang Zhao, Kaiyu Huang, Yue Dong, Degen Huang, Jian-Yun Nie
Venue: Conference CIKM 2024
Year: 2024

Personalized conversational information retrieval (CIR) combines conversational and personalizable elements to satisfy various users' complex information needs through multi-turn interaction based on their backgrounds. The key promise is that the personal textual knowledge base (PTKB) can improve the CIR effectiveness because the retrieval results can be more related to the user's background. However, PTKB is noisy: not every piece of knowledge in PTKB is relevant to the specific query at hand. In this paper, we explore and test several ways to select knowledge from PTKB and use it for query reformulation by using a large language model (LLM). The experimental results show the PTKB might not always improve the search results when used alone, but LLM can help generate a more appropriate personalized query when high-quality guidance is provided.
An exploration of proximity measures in information retrieval
Authors: Tao Tao, ChengXiang Zhai
Venue: Conference SIGIR 2007
Year: 2007

In most existing retrieval models, documents are scored primarily based on various kinds of term statistics such as within-document frequencies, inverse document frequencies, and document lengths. Intuitively, the proximity of matched query terms in a document can also be exploited to promote scores of documents in which the matched query terms are close to each other. Such a proximity heuristic, however, has been largely under-explored in the literature; it is unclear how we can model proximity and incorporate a proximity measure into an existing retrieval model. In this paper,we systematically explore the query term proximity heuristic. Specifically, we propose and study the effectiveness of five different proximity measures, each modeling proximity from a different perspective. We then design two heuristic constraints and use them to guide us in incorporating the proposed proximity measures into an existing retrieval model. Experiments on five standard TREC test collections show that one of the proposed proximity measures is indeed highly correlated with document relevance, and by incorporating it into the KL-divergence language model and the Okapi BM25 model, we can significantly improve retrieval performance.
Thinking inside The Box: Learning Hypercube Representations for Group Recommendation
Authors: Tong Chen, Hongzhi Yin, Jing Long, Quoc Viet Hung Nguyen, Yang Wang, Meng Wang
Venue: Conference SIGIR 2022
Year: 2022

As a step beyond traditional personalized recommendation, group recommendation is the task of suggesting items that can satisfy a group of users. In group recommendation, the core is to design preference aggregation functions to obtain a quality summary of all group members' preferences. Such user and group preferences are commonly represented as points in the vector space (i.e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs. However, the resulted group representations, as points, lack adequate flexibility and capacity to account for the multi-faceted user preferences. Also, the point embedding-based preference aggregation is a less faithful reflection of a group's decision-making process, where all users have to agree on a certain value in each embedding dimension instead of a negotiable interval. In this paper, we propose a novel representation of groups via the notion of hypercubes, which are subspaces containing innumerable points in the vector space. Specifically, we design the hypercube recommender (CubeRec) to adaptively learn group hypercubes from user embeddings with minimal information loss during preference aggregation, and to leverage a revamped distance metric to measure the affinity between group hypercubes and item points. Moreover, to counteract the long-standing issue of data sparsity in group recommendation, we make full use of the geometric expressiveness of hypercubes and innovatively incorporate self-supervision by intersecting two groups. Experiments on four real-world datasets have validated the superiority of CubeRec over state-of-the-art baselines.
Try this instead: Personalized and interpretable substitute recommendation
Authors: Tong Chen 0005, Hongzhi Yin, Guanhua Ye, Zi Huang, Yang Wang 0023, Meng Wang 0001
Venue: Conference SIGIR 2020
Year: 2020

As a fundamental yet significant process in personalized recommendation, candidate generation and suggestion effectively help users spot the most suitable items for them. Consequently, identifying substitutable items that are interchangeable opens up new opportunities to refine the quality of generated candidates. When a user is browsing a specific type of product (e.g., a laptop) to buy, the accurate recommendation of substitutes (e.g., better equipped laptops) can offer the user more suitable options to choose from, thus substantially increasing the chance of a successful purchase. However, existing methods merely treat this problem as mining pairwise item relationships without the consideration of users' personal preferences. Moreover, the substitutable relationships are implicitly identified through the learned latent representations of items, leading to uninterpretable recommendation results. In this paper, we propose attribute-aware collaborative filtering (A2CF) to perform substitute recommendation by addressing issues from both personalization and interpretability perspectives. Instead of directly modelling user-item interactions, we extract explicit and polarized item attributes from user reviews with sentiment analysis, whereafter the representations of attributes, users, and items are simultaneously learned. Then, by treating attributes as the bridge between users and items, we can thoroughly model the user-item preferences (i.e., personalization) and item-item relationships (i.e., substitution) for recommendation. In addition, A2CF is capable of generating intuitive interpretations by analyzing which attributes a user currently cares the most and comparing the recommended substitutes with her/his currently browsed items at an attribute level. The recommendation effectiveness and interpretation quality of A2CF are demonstrated via extensive experiments on three real datasets.
GAME: Learning Graphical and Attentive Multi-view Embeddings for Occasional Group Recommendation
Authors: Zhixiang He, Chi-Yin Chow, Jia-Dong Zhang
Venue: Conference SIGIR 2020
Year: 2020

Group recommendation aims to suggest preferred items to a group of users rather than to an individual user. Most existing methods on group recommendation directly learn theinherent interests of groups and users orinherent features of items, i.e., independently modeling the inherent embeddings of groups, users or items. However, the independent view severely suffers from the cold-start problem when making recommendations for occasional groups that are temporally formed by a set of users and have few interactions on items. Actually, the groups, users and items are interdependent because they interact with one another. The interdependencies constitute an interaction graph that provides multiple views to model the embeddings of groups, users and items from their interacting counterparts to improve recommendation for occasional groups. To this end, we propose a model, named GAME to learn the Graphical and Attentive Multi-view Embeddings (i.e., representations) for the groups, users and items from the independent view and counterpart views based on the interaction graph. In the counterpart views, the embedding of a group, user or item is aggregated from the interacting counterparts based on an attention mechanism that derives the adaptive weight for each counterpart. For instance, a user's embedding may be aggregated from her interacting items or groups. Further, GAME applies neural collaborative filtering to investigate the interactions between the multi-view embeddings of groups (or users) and items for group recommendation. Finally, we conduct extensive experiments on two real datasets. The experimental results show that GAME outperforms other state-of-the-art models, especially on both cold-start groups (i.e., occasional groups) and cold-start items.