Proceedings of the 30th ACM International Conference on Information & Knowledge Management最新文献

librec-auto: A Tool for Recommender Systems Experimentation librec-auto:一个推荐系统实验的工具

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3482006

Nasim Sonboli, M. Mansoury, Ziyue Guo, Shreyas Kadekodi, Weiwen Liu, Zijun Liu, Andrew Schwartz, R. Burke

引用次数: 5

How to Leverage a Multi-layered Transformer Language Model for Text Clustering: an Ensemble Approach 如何利用多层转换语言模型进行文本聚类:一种集成方法

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3482121

Mira Ait-Saada, François Role, M. Nadif

引用次数: 4

SNPR SNPR

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3482394

Mingwei Zhang, Yang Yang, Rizwan Abbas, Ke Deng, Jianxin Li, Bin Zhang

引用次数: 15

Vandalism Detection in OpenStreetMap via User Embeddings 基于用户嵌入的OpenStreetMap故意破坏检测

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3482213

Yinxiao Li, T. J. Anderson, Yiqi Niu

引用次数: 3

Trilateral Spatiotemporal Attention Network for User Behavior Modeling in Location-based Search 基于位置搜索的用户行为建模的三边时空注意网络

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3482206

Yi Qi, Ke Hu, Bo Zhang, Jia Cheng, Jun Lei

{"title":"Trilateral Spatiotemporal Attention Network for User Behavior Modeling in Location-based Search","authors":"Yi Qi, Ke Hu, Bo Zhang, Jia Cheng, Jun Lei","doi":"10.1145/3459637.3482206","DOIUrl":"https://doi.org/10.1145/3459637.3482206","url":null,"abstract":"In location-based search, user's click behavior is naturally bonded with trilateral spatiotemporal information, i.e., the locations of historical user requests, the locations of corresponding clicked items and the occurring time of historical clicks. Appropriate modeling of the trilateral spatiotemporal user click behavior sequence is key to the success of any location-based search service. Though abundant and helpful, existing user behavior modeling methods are insufficient for modeling the rich patterns in trilateral spatiotemporal sequence in that they ignore the interplay among request's geo- graphic information, item's geographic information and the click time. In this work, we study the user behavior modeling problem in location-based search systematically. We propose TRISAN, short for Trilateral Spatiotemporal Attention Network, a novel attention- based neural model that incorporates temporal relatedness into both the modeling of item's geographic closeness and the modeling of request's geographic closeness through a fusion mechanism. In addition, we propose to model the geographic closeness both by distance and by semantic similarity. Extensive experiments demonstrate that the proposed method outperforms existing methods by a large margin and every part of our modeling strategy contributes to its final success.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124937437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Fast k-NN Graph Construction by GPU based NN-Descent 基于GPU的快速k-NN图构建

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3482344

Hui Wang, Wanlei Zhao, Xiangxiang Zeng, Jianye Yang

引用次数: 4

ScarceGAN

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3482474

S. Chakrabarty, Rukma Talwadker, Tridib Mukherjee

{"title":"ScarceGAN","authors":"S. Chakrabarty, Rukma Talwadker, Tridib Mukherjee","doi":"10.1145/3459637.3482474","DOIUrl":"https://doi.org/10.1145/3459637.3482474","url":null,"abstract":"This paper introduces ScarceGAN which focuses on identification of extremely rare or scarce samples from multi-dimensional longitudinal telemetry data with small and weak label prior. We specifically address: (i) severe scarcity in positive class, stemming from both underlying organic skew in the data, as well as extremely limited labels; (ii) multi-class nature of the negative samples, with uneven density distributions and partially overlapping feature distributions; and (iii) massively unlabelled data leading to tiny and weak prior on both positive and negative classes, and possibility of unseen or unknown behavior in the unlabelled set, especially in the negative class. Although related to PU learning problems, we contend that knowledge (or lack of it) on the negative class can be leveraged to learn the compliment of it (i.e., the positive class) better in a semi-supervised manner. To this effect, ScarceGAN re-formulates semi-supervised GAN by accommodating weakly labelled multi- class negative samples and the available positive samples. It relaxes the supervised discriminator's constraint on exact differentiation be- tween negative samples by introducing a 'leeway' term for samples with noisy prior. We propose modifications to the cost objectives of discriminator, in supervised and unsupervised path as well as that of the generator. For identifying risky players in skill gaming, this formulation in whole gives us a recall of over 85% (~60% jump over vanilla semi-supervised GAN) on our scarce class with very minimal verbosity in the unknown space. Further ScarceGAN out- performs the recall benchmarks established by recent GAN based specialized models for the positive imbalanced class identification and establishes a new benchmark in identifying one of rare attack classes (0.09%) in the intrusion dataset from the KDDCUP99 challenge. We establish ScarceGAN to be one of new competitive benchmark frameworks in the rare class identification for longitudinal telemetry data.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123675514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Knowledge Graph Representation Learning as Groupoid: Unifying TransE, RotatE, QuatE, ComplEx Groupoid的知识图表示学习:统一TransE, RotatE, QuatE, ComplEx

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3482442

Han Yang, Junfei Liu

{"title":"Knowledge Graph Representation Learning as Groupoid: Unifying TransE, RotatE, QuatE, ComplEx","authors":"Han Yang, Junfei Liu","doi":"10.1145/3459637.3482442","DOIUrl":"https://doi.org/10.1145/3459637.3482442","url":null,"abstract":"Knowledge graph (KG) representation learning which aims to encode entities and relations into low-dimensional spaces, has been widely used in KG completion and link prediction. Although existing KG representation learning models have shown promising performance, the theoretical mechanism behind existing models is much less well-understood. It is challenging to accurately portray the internal connections between models and build a competitive model systematically. To overcome this problem, a unified KG representation learning framework, called GrpKG, is proposed in this paper to model the KG representation learning from a generic groupoid perspective. We discover that many existing models are essentially the same in the sense of groupoid isomorphism and further provide transformation methods between different models. Moreover, we explore the applications of GrpKG in the model classification as well as other processes. The experiments on several benchmark data sets validate the effectiveness and superiority of our framework by comparing two proposed models (GrpQ8 and GrpM2) with the state-of-the-art models.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125400071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Self-Supervised Learning on Users' Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce 电子商务多场景排序中用户自发行为的自监督学习

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3481953

Yulong Gu, Wentian Bao, Dan Ou, Xiang Li, Baoliang Cui, Biyu Ma, Haikuan Huang, Qingwen Liu, Xiaoyi Zeng

{"title":"Self-Supervised Learning on Users' Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce","authors":"Yulong Gu, Wentian Bao, Dan Ou, Xiang Li, Baoliang Cui, Biyu Ma, Haikuan Huang, Qingwen Liu, Xiaoyi Zeng","doi":"10.1145/3459637.3481953","DOIUrl":"https://doi.org/10.1145/3459637.3481953","url":null,"abstract":"Multi-scenario Learning to Rank is essential for Recommender Systems, Search Engines and Online Advertising in e-commerce portals where the ranking models are usually applied in many scenarios. However, existing works mainly focus on learning the ranking model for a single scenario, and pay less attention to learning ranking models for multiple scenarios. We identify two practical challenges in industrial multi-scenario ranking systems: (1) The Feedback Loop problem that the model is always trained on the items chosen by the ranker itself. (2) Insufficient training data for small and new scenarios. To address the above issues, we present ZEUS, a novel framework that learns a Zoo of ranking modEls for mUltiple Scenarios based on pre-training on users' spontaneous behaviors (e.g. queries which are directly searched in the search box and not recommended by the ranking system). ZEUS decomposes the training process into two stages: self-supervised learning based pre-training and fine-tuning. Firstly, ZEUS performs self-supervised learning on users' spontaneous behaviors and generates a pre-trained model. Secondly, ZEUS fine-tunes the pre-trained model on users' implicit feedback in multiple scenarios. Extensive experiments on Alibaba's production dataset demonstrate the effectiveness of ZEUS, which significantly outperforms state-of-the-art methods. ZEUS averagely achieves 6.0%, 9.7%, 11.7% improvement in CTR, CVR and GMV respectively than state-of-the-art method.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126807648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

RCES RCES

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3481990

Wei Li, Rishi Choudhary, A. Younus, Bruno Ohana, N. Baker, B. Leen, M. A. Qureshi

引用次数: 0