OpenRaiser/Intern-Atlas huggingface dataset:
Recommendations in signed social networks
Authors: Jiliang Tang, Charu C. Aggarwal, Huan Liu
Venue: Conference WWW 2016
Year: 2016

Recommender systems play a crucial role in mitigating the information overload problem in social media by suggesting relevant information to users. The popularity of pervasively available social activities for social media users has encouraged a large body of literature on exploiting social networks for recommendation. The vast majority of these systems focus on unsigned social networks (or social networks with only positive links), while little work exists for signed social networks (or social networks with positive and negative links). The availability of negative links in signed social networks presents both challenges and opportunities in the recommendation process. We provide a principled and mathematical approach to exploit signed social networks for recommendation, and propose a model, RecSSN, to leverage positive and negative links in signed social networks. Empirical results on real-world datasets demonstrate the effectiveness of the proposed framework. We also perform further experiments to explicitly understand the effect of signed networks in RecSSN.
Fast shortest path distance estimation in large networks
Authors: Michalis Potamias, Francesco Bonchi, Carlos Castillo, Aristides Gionis
Venue: Conference CIKM 2009
Year: 2009

In this paper we study approximate landmark-based methods for point-to-point distance estimation in very large networks. These methods involve selecting a subset of nodes as landmarks and computing offline the distances from each node in the graph to those landmarks. At runtime, when the distance between a pair of nodes is needed, it can be estimated quickly by combining the precomputed distances. We prove that selecting the optimal set of landmarks is an NP-hard problem, and thus heuristic solutions need to be employed. We therefore explore theoretical insights to devise a variety of simple methods that scale well in very large networks. The efficiency of the suggested techniques is tested experimentally using five real-world graphs having millions of edges. While theoretical bounds support the claim that random landmarks work well in practice, our extensive experimentation shows that smart landmark selection can yield dramatically more accurate results: for a given target accuracy, our methods require as much as 250 times less space than selecting landmarks at random. In addition, we demonstrate that at a very small accuracy loss our techniques are several orders of magnitude faster than the state-of-the-art exact methods. Finally, we study an application of our methods to the task of social search in large graphs.
Improving personality consistency in conversation by persona extending
Authors: Yifan Liu, Wei Wei, Jiayi Liu, Xianling Mao, Rui Fang, Dangyang Chen
Venue: Conference CIKM 2022
Year: 2022

Endowing chatbots with a consistent personality plays a vital role for agents to deliver human-like interactions. However, existing personalized approaches commonly generate responses in light of static predefined personas depicted with textual description, which may severely restrict the interactivity of human and the chatbot, especially when the agent needs to answer the query excluded in the predefined personas, which is so-called out-of-predefined persona problem (named OOP for simplicity). To alleviate the problem, in this paper we propose a novel retrieval-to-prediction paradigm consisting of two subcomponents, namely, (1) Persona Retrieval Model (PRM), it retrieves a persona from a global collection based on a Natural Language Inference (NLI) model, the inferred persona is consistent with the predefined personas; and (2) Posterior-scored Transformer (PS-Transformer), it adopts a persona posterior distribution that further considers the actual personas used in the ground response, maximally mitigating the gap between training and inferring. Furthermore, we present a dataset called IT-ConvAI2 that first highlights the OOP problem in personalized dialogue. Extensive experiments on both IT-ConvAI2 and ConvAI2 demonstrate that our proposed model yields considerable improvements in both automatic metrics and human evaluations.
Constrained sequence-to-tree generation for hierarchical text classification
Authors: Chao Yu, Yi Shen, Yue Mao
Venue: Conference SIGIR 2022
Year: 2022

Hierarchical Text Classification (HTC) is a challenging task where a document can be assigned to multiple hierarchically structured categories within a taxonomy. The majority of prior studies consider HTC as a flat multi-label classification problem, which inevitably leads to ''label inconsistency'' problem. In this paper, we formulate HTC as a sequence generation task and introduce a sequence-to-tree framework (Seq2Tree) for modeling the hierarchical label structure. Moreover, we design a constrained decoding strategy with dynamic vocabulary to secure the label consistency of the results. Compared with previous works, the proposed approach achieves significant and consistent improvements on three benchmark datasets.
Taxocomplete: Self-supervised taxonomy completion leveraging position-enhanced semantic matching
Authors: Ines Arous, Ljiljana Dolamic, Philippe Cudré-Mauroux
Venue: Conference WWW 2023
Year: 2023

Taxonomies are used to organize knowledge in many applications, including recommender systems, content browsing, or web search. With the emergence of new concepts, static taxonomies become obsolete as they fail to capture up-to-date knowledge. Several approaches have been proposed to address the problem of maintaining taxonomies automatically. These approaches typically rely on a limited set of neighbors to represent a given node in the taxonomy. However, considering distant nodes could improve the representation of some portions of the taxonomy, especially for those nodes situated in the periphery or in sparse regions of the taxonomy. In this work, we propose TaxoComplete, a self-supervised taxonomy completion framework that learns the representation of nodes leveraging their position in the taxonomy. TaxoComplete uses a self-supervision generation process that selects some nodes and associates each of them with an anchor set, which is a set composed of nodes in the close and distant neighborhood of the selected node. Using self-supervision data, TaxoComplete learns a position-enhanced node representation using two components: (1) a query-anchor semantic matching mechanism, which encodes pairs of nodes and matches their semantic distance to their graph distance, such that nodes that are close in the taxonomy are placed closely in the shared embedding space while distant nodes are placed further apart; (2) a direction-aware propagation module, which embeds the direction of edges in node representation, such that we discriminate relation from other taxonomic relations. Our approach allows the representation of nodes to encapsulate information from a large neighborhood while being aware of the distance separating pairs of nodes in the taxonomy. Extensive experiments on four real-world and large-scale datasets show that TaxoComplete is substantially more effective than state-of-the-art methods (2x more effective in terms of HIT@k).
Expanding taxonomies with implicit edge semantics
Authors: Emaad A. Manzoor, Rui Li, Dhananjay Shrouty, Jure Leskovec
Venue: Conference WWW 2020
Year: 2020

Curated taxonomies enhance the performance of machine-learning systems via high-quality structured knowledge. However, manually curating a large and rapidly-evolving taxonomy is infeasible. In this work, we propose Arborist, an approach to automatically expand textual taxonomies by predicting the parents of new taxonomy nodes. Unlike previous work, Arborist handles the more challenging scenario of taxonomies with heterogeneous edge semantics that are unobserved. Arborist learns latent representations of the edge semantics along with embeddings of the taxonomy nodes to measure taxonomic relatedness between node pairs. Arborist is then trained by optimizing a large-margin ranking loss with a dynamic margin function. We propose a principled formulation of the margin function, which theoretically guarantees that Arborist minimizes an upper-bound on the shortest-path distance between the predicted parents and actual parents in the taxonomy. Via extensive evaluation on a curated taxonomy at Pinterest and several public datasets, we demonstrate that Arborist outperforms the state-of-the-art, achieving up to 59% in mean reciprocal rank and 83% in recall at 15. We also explore the ability of Arborist to infer nodes’ taxonomic-roles, without explicit supervision on this task.
QEN: Applicable Taxonomy Completion via Evaluating Full Taxonomic Relations
Authors: Suyuchen Wang, Ruihui Zhao, Yefeng Zheng, Bang Liu
Venue: Conference WWW 2022
Year: 2022

Taxonomy is a fundamental type of knowledge graph for a wide range of web applications like searching and recommendation systems. To keep a taxonomy automatically updated with the latest concepts, the taxonomy completion task matches a pair of proper hypernym and hyponym in the original taxonomy with the new concept as its parent and child. Previous solutions utilize term embeddings as input and only evaluate the parent-child relations between the new concept and the hypernym-hyponym pair. Such methods ignore the important sibling relations, and are not applicable in reality since term embeddings are not available for the latest concepts. They also suffer from the relational noise of the “pseudo-leaf” node, which is a null node acting as a node’s hyponym to enable the new concept to be a leaf node. To tackle the above drawbacks, we propose the Quadruple Evaluation Network (QEN), a novel taxonomy completion framework that utilizes easily accessible term descriptions as input, and applies pretrained language model and code attention for accurate inference while reducing online computation. QEN evaluates both parent-child and sibling relations to both enhance the accuracy and reduce the noise brought by pseudo-leaf. Extensive experiments on three real-world datasets in different domains with different sizes and term description sources prove the effectiveness and robustness of QEN on overall performance and especially the performance for adding non-leaf nodes, which largely surpasses previous methods and achieves the new state-of-the-art of the task.1
A sequence-to-sequence approach with mixed pointers to topic segmentation and segment labeling
Authors: Jinxiong Xia, Houfeng Wang
Venue: Conference KDD 2023
Year: 2023

Topic segmentation is the process of dividing a text into semantically coherent segments, and segment labeling involves assigning a topic label to each of these segments. Previous work on this task has included the use of sequence labeling, segment-extraction, and generative models. While these methods have yielded impressive results, existing generative models have struggled to accurately generate strings of segment boundaries, limiting their competitiveness in this area. In this paper, we present a novel Sequence-to-Sequence approach with Mixed Pointers (Seq2Seq-MP). Seq2Seq-MP employs an encoder-decoder architecture with the pointer mechanism to generate both segment boundaries and topics, which allows for a more robust performance than string-generation models and can handle long-range dependencies better than sequence labeling and segment-extraction models. Additionally, we introduce the pairwise type encoding and type-aware relative position encoding to improve the fusion of type and position information, enhancing the interactions between sentences and topics in the encoder and decoder. Our experiments on public datasets show that Seq2Seq-MP outperforms the current state-of-the-art, with up to 2.9% and 4.0% improvements in Pk and F1, respectively.
Ctcam: Enhancing transportation evaluation through fusion of cellular traffic and camera-based vehicle flows
Authors: ChungYi Lin, Shen-Lung Tung, Hung-Ting Su, Winston H. Hsu
Venue: Conference CIKM 2023
Year: 2023

Traffic prediction utility often faces infrastructural limitations, which restrict its coverage. To overcome this challenge, we present Geographical Cellular Traffic (GCT) flow that leverages cellular network data as a new source for transportation evaluation. The broad coverage of cellular networks allows GCT flow to capture various mobile user activities across regions, aiding city authorities in resource management through precise predictions. Acknowledging the complexity arising from the diversity of mobile users in GCT flow, we supplement it with camera-based vehicle flow data from limited deployments and verify their spatio-temporal attributes and correlations through extensive data analysis. Our two-stage fusion approach integrates these multi-source data, addressing their coverage and magnitude discrepancies, thereby enhancing the prediction of GCT flow for accurate transportation evaluation. Overall, we propose novel uses of telecom data in transportation and verify its effectiveness in multi-source fusion with vision-based data.
Personalized showcases: Generating multi-modal explanations for recommendations
Authors: An Yan, Zhankui He, Jiacheng Li, Tianyang Zhang, Julian John McAuley
Venue: Conference SIGIR 2023
Year: 2023

Existing explanation models generate only text for recommendations but still struggle to produce diverse contents. In this paper, to further enrich explanations, we propose a new task named personalized showcases, in which we provide both textual and visual information to explain our recommendations. Specifically, we first select a personalized image set that is the most relevant to a user's interest toward a recommended item. Then, natural language explanations are generated accordingly given our selected images. For this new task, we collect a large-scale dataset from Google Local (i.e.,~maps) and construct a high-quality subset for generating multi-modal explanations. We propose a personalized multi-modal framework which can generate diverse and visually-aligned explanations via contrastive learning. Experiments show that our framework benefits from different modalities as inputs, and is able to produce more diverse and expressive explanations compared to previous methods on a variety of evaluation metrics.
Revisiting approximate metric optimization in the age of deep neural networks
Authors: Sebastian Bruch, Masrour Zoghi, Michael Bendersky, Marc Najork
Venue: Conference SIGIR 2019
Year: 2019

Learning-to-Rank is a branch of supervised machine learning that seeks to produce an ordering of a list of items such that the utility of the ranked list is maximized. Unlike most machine learning techniques, however, the objective cannot be directly optimized using gradient descent methods as it is either discontinuous or flat everywhere. As such, learning-to-rank methods often optimize a loss function that either is loosely related to or upper-bounds a ranking utility instead. A notable exception is the approximation framework originally proposed by Qin et al. that facilitates a more direct approach to ranking metric optimization. We revisit that framework almost a decade later in light of recent advances in neural networks and demonstrate its superiority empirically. Through this study, we hope to show that the ideas from that work are more relevant than ever and can lay the foundation of learning-to-rank research in the age of deep neural networks.
Efficient maximum clique computation over large sparse graphs
Authors: Lijun Chang
Venue: Conference KDD 2019
Year: 2019

This paper studies the problem of MCC-Sparse, Maximum Clique Computation over large real-world graphs that are usually Sparse. In the literature, MCC-Sparse has been studied separately and less extensively than its dense counterpart MCC-Dense, and advanced algorithmic techniques that are developed for MCC-Dense have not been utilized in the existing MCC-Sparse solvers. In this paper, we design an algorithm MC-BRB which transforms an instance of MCC-Sparse to instances of k-clique finding over dense subgraphs (KCF-Dense) that can be computed by the existing MCC-Dense solvers. To further improve the efficiency, we then develop a new branch-reduce-&-bound framework for KCF-Dense by proposing light-weight reducing techniques and leveraging the existing advanced branching and bounding techniques of MCC-Dense solvers. In addition, we also design an ego-centric algorithm MC-EGO for heuristically computing a near-maximum clique in near-linear time. We conduct extensive empirical studies on large real graphs and demonstrate the efficiency and effectiveness of our techniques.
Algorithmic fairness generalization under covariate and dependence shifts simultaneously
Authors: Chen Zhao 0010, Kai Jiang 0002, Xintao Wu, Haoliang Wang, Latifur Khan, Christan Grant, Feng Chen 0001
Venue: Conference KDD 2024
Year: 2024

The endeavor to preserve the generalization of a fair and invariant classifier across domains, especially in the presence of distribution shifts, becomes a significant and intricate challenge in machine learning. In response to this challenge, numerous effective algorithms have been developed with a focus on addressing the problem of fairness-aware domain generalization. These algorithms are designed to navigate various types of distribution shifts, with a particular emphasis on covariate and dependence shifts. In this context, covariate shift pertains to changes in the marginal distribution of input features, while dependence shift involves alterations in the joint distribution of the label variable and sensitive attributes. In this paper, we introduce a simple but effective approach that aims to learn a fair and invariant classifier by simultaneously addressing both covariate and dependence shifts across domains. We assert the existence of an underlying transformation model can transform data from one domain to another, while preserving the semantics related to non-sensitive attributes and classes. By augmenting various synthetic data domains through the model, we learn a fair and invariant classifier in source domains. This classifier can then be generalized to unknown target domains, maintaining both model prediction and fairness concerns. Extensive empirical studies on four benchmark datasets demonstrate that our approach surpasses state-of-the-art methods.
An interpretable brain graph contrastive learning framework for brain disorder analysis
Authors: Xuexiong Luo, Guangwei Dong, Jia Wu, Amin Beheshti, Jian Yang, Shan Xue
Venue: Conference WSDM 2024
Year: 2024

In this paper, we propose an interpretable brain graph contrastive learning framework, which aims to learn brain graph representations by a unsupervised way for disorder prediction and pathogenic analysis. Our framework consists of two key designs: We first utilize the controllable data augmentation strategy to perturb unimportant structures and attribute features for the generation of brain graphs. Then, considering that the difference of healthy and patient brain graphs is small, we introduce hard negative sample evaluation to weight negative samples of the contrastive loss, which can learn more discriminative brain graph representations. More importantly, our method can observe salient brain regions and connections for pathogenic analysis. We conduct disorder prediction and interpretable analysis experiments on three real-world neuroimaging datasets to demonstrate the effectiveness of our framework.
DCDIR: A deep cross-domain recommendation system for cold start users in insurance domain
Authors: Ye Bi, Liqiang Song, Mengqiu Yao, Zhenyu Wu, Jianming Wang, Jing Xiao 0006
Venue: Conference SIGIR 2020
Year: 2020

Internet insurance products are apparently different from traditional e-commerce goods for their complexity, low purchasing frequency, etc. So, cold start problem is even worse. In traditional e-commerce field, several cross-domain recommendation (CDR) methods have been studied to infer preferences of cold start users based on their preferences in other domains. However, these CDR methods couldn't be applied into insurance domain directly due to product complexity. In this paper, we propose a Deep Cross-Domain Insurance Recommendation System (DCDIR) for cold start users. Specifically, we first learn more effective user and item latent features in both domains. In target domain, given the complexity of insurance products, we design a meta-path based method over insurance product knowledge graph. In source domain, we employ GRU to model users' dynamic interests. Then we learn a feature mapping function by multi-layer perceptions. We apply DCDIR on our company's dataset, and show DCDIR significantly outperforms the state-of-the-art solutions.
Dual attention transfer in session-based recommendation with multi-dimensional integration
Authors: Chen Chen 0128, Jie Guo 0008, Bin Song 0001
Venue: Conference SIGIR 2021
Year: 2021

Session-based recommendation (SBR) is widely used in e-commerce to predict the anonymous user's next click action according to a short sequence. Many previous studies have shown the potential advantages of applying Graph Neural Networks (GNN) to SBR tasks. However, the existing SBR models using GNN to solve user preference problems are only based on one single dataset to obtain one recommendation model during training. While the single dataset has the problems including the excessive sparse data source and the long-distance relationship of items. Therefore, introducing the dual transfer, which can enrich the data source, to SBR is absolutely necessary. To this end, a new method is proposed in this paper, which is called dual attention transfer based on multi-dimensional integration (DAT-MDI): (i) DAT uses a potential mapping method based on a slot attention mechanism to extract the user's representation information in different sessions between multiple domains. (ii) MDI combines the graph neural network for the graphs (session graph and global graph) and the gate recurrent unit (GRU) for the sequence to learn the item representation in each session. Then the multi-level session representation are combined by a soft-attention mechanism. We do a variety of experiments on four benchmark datasets which have shown that the superiority of the DAT-MDI model over the state-of-the-art methods.
Cross domain lifelong sequential modeling for online click-through rate prediction
Authors: Ruijie Hou, Zhaoyang Yang, Ming Yu, Hongyu Lu, Zhuobin Zheng, Yu Chen, Qinsong Zeng, Ming Chen
Venue: Conference KDD 2024
Year: 2024

Lifelong sequential modeling (LSM) has significantly advanced recommendation systems on social media platforms. Diverging from single-domain LSM, cross-domain LSM involves modeling lifelong behavior sequences from a source domain to a different target domain. In this paper, we propose the Lifelong Cross Network (LCN), a novel approach for cross-domain LSM. LCN features a Cross Representation Production (CRP) module that utilizes contrastive loss to improve the learning of item embeddings, effectively bridging items across domains. This is important for enhancing the retrieval of relevant items in cross-domain lifelong sequences. Furthermore, we propose the Lifelong Attention Pyramid (LAP) module, which contains three cascading attention levels. By adding an intermediate level and integrating the results from all three levels, the LAP module can capture a broad spectrum of user interests and ensure gradient propagation throughout the sequence. The proposed LAP can also achieve remarkable consistency across attention levels, making it possible to further narrow the candidate item pool of the top level. This allows for the use of advanced attention techniques to effectively mitigate the impact of the noise in cross-domain sequences and improve the non-linearity of the representation, all while maintaining computational efficiency. Extensive experiments conducted on both a public dataset and an industrial dataset from the WeChat Channels platform reveal that the LCN outperforms current methods in terms of prediction accuracy and online performance metrics.