ACM Transactions on the Web最新文献

{"title":"Enhancing Conversational Recommendation Systems with Representation Fusion","authors":"Yingxu Wang, Xiaoru Chen, Jinyuan Fang, Zaiqiao Meng, Shangsong Liang","doi":"https://dl.acm.org/doi/10.1145/3577034","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3577034","url":null,"abstract":"<p>Conversational Recommendation Systems (CRSs) aim to improve recommendation performance by utilizing information from a conversation session. A CRS first constructs questions and then asks users for their feedback in each conversation session to refine better recommendation lists to users. The key design of CRS is to construct proper questions and obtain users’ feedback in response to these questions so as to effectively capture user preferences. Many CRS works have been proposed; however, they suffer from defects when constructing questions for users to answer: (1) employing a dialogue policy agent for constructing questions is one of the most common choices in CRS, but it needs to be trained with a huge corpus, and (2) it is not appropriate that constructing questions from a single policy (e.g., a CRS only selects attributes that the user has interacted with) for all users with different preferences. To address these defects, we propose a novel CRS model, namely a Representation Fusion–based Conversational Recommendation model, where the whole conversation session is divided into two subsessions (i.e., Local Question Search subsession and Global Question Search subsession) and two different question search methods are proposed to construct questions in the corresponding subsessions without employing policy agents. In particular, in the Local Question Search subsession we adopt a novel graph mining method to find questions, where the paths in the graph between users and attributes can eliminate irrelevant attributes; in the Global Question Search subsession we propose to initialize user preference on items with the user and all item historical rating records and construct questions based on user’s preference. Then, we update the embeddings independently over the two subsessions according to user’s feedback and fuse the final embeddings from the two subsessions for the recommendation. Experiments on three real-world recommendation datasets demonstrate that our proposed method outperforms five state-of-the-art baselines.</p>","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138495127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning Neighbor User Intention on User-Item Interaction Graphs for Better Sequential Recommendation","authors":"Mei Yu, Kun Zhu, Mankun Zhao, Jian Yu, Tianyi Xu, Di Jin, Xuewei Li, Ruiguo Yu","doi":"https://dl.acm.org/doi/10.1145/3580520","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3580520","url":null,"abstract":"<p>The task of Sequential Recommendation aims to predict the user’s preference by analyzing the user’s historical behaviours. Existing methods model item transitions through leveraging sequential patterns. However, they mainly consider the target user’s own behaviours and dynamic characteristics, while often ignore the high-order collaborative connections when modelling user preferences. Some recent works try to use graph-based methods to introduce high-order collaborative signals for Sequential Recommendation, but they have two main problems. One is that the sequential patterns cannot be effectively mined, and the other is that their way of introducing high-order collaborative signals is not very suitable for Sequential Recommendation. To address these problems, we propose to fully exploit sequence features and model high-order collaborative signals for Sequential Recommendation. We propose a <b>N</b>eighbor user <b>I</b>ntention based <b>S</b>equential <b>Rec</b>ommender, namely NISRec, which utilizes the intentions of high-order connected neighbor users as high-order collaborative signals, in order to improve recommendation performance for the target user. To be specific, NISRec contains two main modules: the neighbor user intention embedding module (NIE) and the fusion module. The NIE describes both the long-term and the short-term intentions of neighbor users and aggregates them separately. The fusion module uses these two types of aggregated intentions to model high-order collaborative signals in both the embedding process and the user preference modelling phase for recommendation of the target user. Experimental results show that our new approach outperforms the state-of-the-art methods on both sparse and dense datasets. Extensive studies further show the effectiveness of the diverse neighbor intentions introduced by NISRec.</p>","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138516923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Task Graph Neural Network with Variational Graph Auto-Encoders for Session-Based Travel Packages Recommendation","authors":"Guixiang Zhu, Jie Cao, Lei Chen, Youquan Wang, Zhan Bu, Shuxin Yang, Jianqing Wu, Zhiping Wang","doi":"10.1145/3577032","DOIUrl":"https://doi.org/10.1145/3577032","url":null,"abstract":"Session-based travel packages recommendation aims to predict users’ next click based on their current and historical sessions recorded by Online Travel Agencies (OTAs). Recently, an increasing number of studies attempted to apply Graph Neural Networks (GNNs) to the session-based recommendation and obtained promising results. However, most of them do not take full advantage of the explicit latent structure from attributes of items, making learned representations of items less effective and difficult to interpret. Moreover, they only combine historical sessions (long-term preferences) with a current session (short-term preference) to learn a unified representation of users, ignoring the effects of historical sessions for the current session. To this end, this article proposes a novel session-based model named STR-VGAE, which fills subtasks of the travel packages recommendation and variational graph auto-encoders simultaneously. STR-VGAE mainly consists of three components: travel packages encoder, users behaviors encoder, and interaction modeling. Specifically, the travel packages encoder module is used to learn a unified travel package representation from co-occurrence attribute graphs by using multi-view variational graph auto-encoders and a multi-view attention network. The users behaviors encoder module is used to encode user’ historical and current sessions with a personalized GNN, which considers the effects of historical sessions on the current session, and coalesce these two kinds of session representations to learn the high-quality users’ representations by exploiting a gated fusion approach. The interaction modeling module is used to calculate recommendation scores over all candidate travel packages. Extensive experiments on a real-life tourism e-commerce dataset from China show that STR-VGAE yields significant performance advantages over several competitive methods, meanwhile provides an interpretation for the generated recommendation list.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43010479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning Neighbor User Intention on User-Item Interaction Graphs for Better Sequential Recommendation","authors":"Mei Yu, Kun Zhu, Mankun Zhao, Jian Yu, Tianyi Xu, Di Jin, Xuewei Li, Ruiguo Yu","doi":"10.1145/3580520","DOIUrl":"https://doi.org/10.1145/3580520","url":null,"abstract":"The task of Sequential Recommendation aims to predict the user’s preference by analyzing the user’s historical behaviours. Existing methods model item transitions through leveraging sequential patterns. However, they mainly consider the target user’s own behaviours and dynamic characteristics, while often ignore the high-order collaborative connections when modelling user preferences. Some recent works try to use graph-based methods to introduce high-order collaborative signals for Sequential Recommendation, but they have two main problems. One is that the sequential patterns cannot be effectively mined, and the other is that their way of introducing high-order collaborative signals is not very suitable for Sequential Recommendation. To address these problems, we propose to fully exploit sequence features and model high-order collaborative signals for Sequential Recommendation. We propose a Neighbor user Intention based Sequential Recommender, namely NISRec, which utilizes the intentions of high-order connected neighbor users as high-order collaborative signals, in order to improve recommendation performance for the target user. To be specific, NISRec contains two main modules: the neighbor user intention embedding module (NIE) and the fusion module. The NIE describes both the long-term and the short-term intentions of neighbor users and aggregates them separately. The fusion module uses these two types of aggregated intentions to model high-order collaborative signals in both the embedding process and the user preference modelling phase for recommendation of the target user. Experimental results show that our new approach outperforms the state-of-the-art methods on both sparse and dense datasets. Extensive studies further show the effectiveness of the diverse neighbor intentions introduced by NISRec.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44213438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding the Kodi Ecosystem","authors":"Yunming Xiao, Matteo Varvello, Marc Warrior, Aleksandar Kuzmanovic","doi":"https://dl.acm.org/doi/10.1145/3563700","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3563700","url":null,"abstract":"<p>Free and open-source media centers are experiencing a boom in popularity for the convenience they offer users seeking to remotely consume digital content. Kodi is today’s most popular home media center, with millions of users worldwide. Kodi’s popularity derives from its ability to centralize the sheer amount of media content available on the Web, both <i>free</i> and <i>copyrighted</i>. Researchers have been hinting at potential security concerns around Kodi, due to <i>add-ons</i> injecting unwanted content as well as user settings linked with security holes. Motivated by these observations, this article conducts the first comprehensive analysis of the Kodi ecosystem: 15,000 Kodi users from 104 countries, 11,000 unique add-ons, and data collected over 9 months.</p><p>Our work makes three important contributions. Our first contribution is that we build “crawling” software (<monospace>de-Kodi</monospace>) which can automatically install a Kodi add-on, explore its menu, and locate (video) content. This is challenging for two main reasons. First, Kodi largely relies on visual information and user input which intrinsically complicates automation. Second, the potential sheer size of this ecosystem (i.e., the number of available add-ons) requires a highly scalable crawling solution. Our second contribution is that we develop a solution to discover Kodi add-ons. Our solution combines Web crawling of popular websites where Kodi add-ons are published (LazyKodi and GitHub) and <monospace>SafeKodi</monospace>, a Kodi add-on we have developed which leverages the help of Kodi users to learn which add-ons are used in the wild and, in return, offers information about how <i>safe</i> these add-ons are, e.g., do they track user activity or contact sketchy URLs/IP addresses. Our third contribution is a classifier to passively detect Kodi traffic and add-on usage in the wild.</p><p>Our analysis of the Kodi ecosystem reveals the following findings. We find that most installed add-ons are <i>unofficial</i> but <i>safe</i> to use. Still, 78% of the users have installed at least one <i>unsafe</i> add-on, and even worse, such add-ons are among the most popular. In response to the information offered by SafeKodi, one-third of the users reacted by disabling some of their add-ons. However, the majority of users ignored our warnings for several months attracted by the content such unsafe add-ons have to offer. Last but not least, we show that Kodi’s auto-update, a feature active for 97.6% of SafeKodi users, makes Kodi users easily identifiable by their ISPs. While passively identifying which Kodi add-on is in use is, as expected, much harder, we also find that many unofficial add-ons do not use HTTPS yet, making their passive detection straightforward.¹</p>","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138495124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Adaptive Graph Clustering via von Mises-Fisher Distributions","authors":"Pengfei Wang, Daqing Wu, Chong Chen, Kunpeng Liu, Yanjie Fu, Jianqiang Huang, Yuanchun Zhou, Jianfeng Zhan, Xiansheng Hua","doi":"https://dl.acm.org/doi/10.1145/3580521","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3580521","url":null,"abstract":"<p>Graph clustering has been a hot research topic and is widely used in many fields, such as community detection in social networks. Lots of works combining auto-encoder and graph neural networks have been applied to clustering tasks by utilizing node attributes and graph structure. These works usually assumed the inherent parameters (i.e. size and variance) of different clusters in the latent embedding space are homogeneous, and hence the assigned probability is monotonous over the Euclidean distance between node embeddings and centroids. Unfortunately, this assumption usually does not hold since the size and concentration of different clusters can be quite different, which limits the clustering accuracy. In addition, the node embeddings in deep graph clustering methods are usually L2 normalized so that it lies on the surface of a unit hyper-sphere. To solve this problem, we proposed <underline><b>D</b></underline>eep <underline><b>A</b></underline>daptive <underline><b>G</b></underline>raph <underline><b>C</b></underline>lustering via von Mises-Fisher distributions, namely DAGC. DAGC assumes the node embeddings <b>H</b> can be drawn from a von Mises-Fisher distribution and each cluster <i>k</i> is associated with cluster inherent parameters <b><i>ρ</i></b>_<i>k</i> which includes cluster center <b><i>μ</i></b> and cluster cohesion degree <i>κ</i>. Then we adopt an EM-like approach (i.e. (mathcal {P}(mathbf {H}|mathbf {rho }) ) and (mathcal {P}(mathbf {rho }|mathbf {H}) ) respectively) to learn the embedding and cluster inherent parameters alternately. Specifically, with the node embeddings, we proposed to update the cluster centers in an attraction-repulsion manner to make the cluster centers more separable. And given the cluster inherent parameters, a likelihood-based loss is proposed to make node embeddings more concentrated around cluster centers. Thus, DAGC can simultaneously improve the intra-cluster compactness and inter-cluster heterogeneity. Finally, extensive experiments conducted on four benchmark datasets have demonstrated that the proposed DAGC consistently outperforms the state-of-the-art methods, especially on imbalanced datasets.</p>","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138495123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Adaptive Graph Clustering via von Mises-Fisher Distributions","authors":"P. Wang, Daqing Wu, Chong Chen, Kunpeng Liu, Yanjie Fu, Jianqiang Huang, Yuanchun Zhou, Jianfeng Zhan, Xiansheng Hua","doi":"10.1145/3580521","DOIUrl":"https://doi.org/10.1145/3580521","url":null,"abstract":"Graph clustering has been a hot research topic and is widely used in many fields, such as community detection in social networks. Lots of works combining auto-encoder and graph neural networks have been applied to clustering tasks by utilizing node attributes and graph structure. These works usually assumed the inherent parameters (i.e. size and variance) of different clusters in the latent embedding space are homogeneous, and hence the assigned probability is monotonous over the Euclidean distance between node embeddings and centroids. Unfortunately, this assumption usually does not hold since the size and concentration of different clusters can be quite different, which limits the clustering accuracy. In addition, the node embeddings in deep graph clustering methods are usually L2 normalized so that it lies on the surface of a unit hyper-sphere. To solve this problem, we proposed Deep Adaptive Graph Clustering via von Mises-Fisher distributions, namely DAGC. DAGC assumes the node embeddings H can be drawn from a von Mises-Fisher distribution and each cluster k is associated with cluster inherent parameters ρk which includes cluster center μ and cluster cohesion degree κ. Then we adopt an EM-like approach (i.e. (mathcal {P}(mathbf {H}|mathbf {rho }) ) and (mathcal {P}(mathbf {rho }|mathbf {H}) ) respectively) to learn the embedding and cluster inherent parameters alternately. Specifically, with the node embeddings, we proposed to update the cluster centers in an attraction-repulsion manner to make the cluster centers more separable. And given the cluster inherent parameters, a likelihood-based loss is proposed to make node embeddings more concentrated around cluster centers. Thus, DAGC can simultaneously improve the intra-cluster compactness and inter-cluster heterogeneity. Finally, extensive experiments conducted on four benchmark datasets have demonstrated that the proposed DAGC consistently outperforms the state-of-the-art methods, especially on imbalanced datasets.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45250922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constructing Spatio-Temporal Graphs for Face Forgery Detection","authors":"Zhihua Shang, Hongtao Xie, Lingyun Yu, Zhengjun Zha, Yongdong Zhang","doi":"10.1145/3580512","DOIUrl":"https://doi.org/10.1145/3580512","url":null,"abstract":"Recently, advanced development of facial manipulation techniques threatens web information security, thus, face forgery detection attracts a lot of attention. It is clear that both spatial and temporal information of facial videos contains the crucial manipulation traces, which are inevitably created during the generation process. However, most existing face forgery detectors only focus on the spatial artifacts or the temporal incoherence, and they are struggling to learn a significant and general kind of representations for manipulated facial videos. In this work, we propose to construct spatial-temporal graphs for fake videos to capture the spatial inconsistency and the temporal incoherence at the same time. To model the spatial-temporal relationship among the graph nodes, a novel forgery detector named Spatio-Temporal Graph Network (STGN) is proposed, which contains two kinds of graph-convolution-based units, the Spatial Relation Graph Unit (SRGU) and the Temporal Attention Graph Unit (TAGU). To exploit spatial information, the SRGU models the inconsistency between each pair of patches in the same frame, instead of focusing on the low-level local spatial artifacts which are vulnerable to samples created by unseen manipulation methods. And, the TAGU is proposed to model the long-distance temporal relation among the patches at the same spatial position in different frames with a graph attention mechanism based on the inter-node similarity. With the SRGU and the TAGU, our STGN can combine the discriminative power of spatial inconsistency and the generalization capacity of temporal incoherence for face forgery detection. Our STGN achieves state-of-the-art performances on several popular forgery detection datasets. Extensive experiments demonstrate both the superiority of our STGN on intra manipulation evaluation and the effectiveness for new sorts of face forgery videos on cross manipulation evaluation.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44803752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contrastive Graph Similarity Networks","authors":"Luzhi Wang, Yizhen Zheng, Di Jin, Fuyi Li, Yongliang Qiao, Shirui Pan","doi":"10.1145/3580511","DOIUrl":"https://doi.org/10.1145/3580511","url":null,"abstract":"Graph similarity learning is a significant and fundamental issue in the theory and analysis of graphs, which has been applied in a variety of fields, including object tracking, recommender systems, similarity search, etc. Recent methods for graph similarity learning that utilize deep learning typically share two deficiencies: (1) they leverage graph neural networks as backbones for learning graph representations but have not well captured the complex information inside data, and (2) they employ a cross-graph attention mechanism for graph similarity learning, which is computationally expensive. Taking these limitations into consideration, a method for graph similarity learning is devised in this study, namely, Contrastive Graph Similarity Network (CGSim). To enhance graph similarity learning, CGSim makes use of the complementary information of two input graphs and captures pairwise relations in a contrastive learning framework. By developing a dual contrastive learning module with a node-graph matching and a graph-graph matching mechanism, our method significantly reduces the quadratic time complexity for cross-graph interaction modeling to linear time complexity. Jointly learning in an end-to-end framework, the graph representation embedding module and the well-designed contrastive learning module can be beneficial to one another. A comprehensive series of experiments indicate that CGSim outperforms state-of-the-art baselines on six datasets and significantly reduces the computational cost, which demonstrates our CGSim model’s superiority over other baselines.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43054099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heterogeneous Graph Transformer for Meta-structure Learning with Application in Text Classification","authors":"Shuhai Wang, Xin Liu, Xiao-Bin Pan, Hanjie Xu, Mingrui Liu","doi":"10.1145/3580508","DOIUrl":"https://doi.org/10.1145/3580508","url":null,"abstract":"The prevalent heterogeneous Graph Neural Network (GNN) models learn node and graph representations using pre-defined meta-paths or only automatically discovering meta-paths. However, the existing methods suffer from information loss due to neglecting undiscovered meta-structures with richer semantics than meta-paths in heterogeneous graphs. To take advantage of the current rich meta-structures in heterogeneous graphs, we propose a novel approach called HeGTM to automatically extract essential meta-structures (i.e., meta-paths and meta-graphs) from heterogeneous graphs. The discovered meta-structures can capture more prosperous relations between different types of nodes that can help the model to learn representations. Furthermore, we apply the proposed approach for text classification. Specifically, we first design a heterogeneous graph for the text corpus, and then apply HeGTM on the constructed text graph to learn better text representations that contain various semantic relations. In addition, our approach can also be used as a strong meta-structure extractor for other GNN models. In other words, the auto-discovered meta-structures can replace the pre-defined meta-paths. The experimental results on text classification demonstrate the effectiveness of our approach to automatically extracting informative meta-structures from heterogeneous graphs and its usefulness in acting as a meta-structure extractor for boosting other GNN models.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44890406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}