Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining最新文献_第7页

RecWalk: Nearly Uncoupled Random Walks for Top-N Recommendation RecWalk:用于Top-N推荐的几乎不耦合随机行走

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291016

A. Nikolakopoulos, G. Karypis

{"title":"RecWalk: Nearly Uncoupled Random Walks for Top-N Recommendation","authors":"A. Nikolakopoulos, G. Karypis","doi":"10.1145/3289600.3291016","DOIUrl":"https://doi.org/10.1145/3289600.3291016","url":null,"abstract":"Random walks can provide a powerful tool for harvesting the rich network of interactions captured within item-based models for top-n recommendation. They can exploit indirect relations between the items, mitigate the effects of sparsity, ensure wider itemspace coverage, as well as increase the diversity of recommendation lists. Their potential however, is hindered by the tendency of the walks to rapidly concentrate towards the central nodes of the graph, thereby significantly restricting the range of K-step distributions that can be exploited for personalized recommendations. In this work we introduce RecWalk; a novel random walk-based method that leverages the spectral properties of nearly uncoupled Markov chains to provably lift this limitation and prolong the influence of users' past preferences on the successive steps of the walk--allowing the walker to explore the underlying network more fruitfully. A comprehensive set of experiments on real-world datasets verify the theoretically predicted properties of the proposed approach and indicate that they are directly linked to significant improvements in top-n recommendation accuracy. They also highlight RecWalk's potential in providing a framework for boosting the performance of item-based models. RecWalk achieves state-of-the-art top-n recommendation quality outperforming several competing approaches, including recently proposed methods that rely on deep neural networks.","PeriodicalId":143253,"journal":{"name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126192824","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: 61

Session details: Session 5: Understanding Conversation, Discussion, and Opinions 第五部分:理解对话、讨论和观点

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3310345

Hang Li Bytedance

引用次数: 0

Attending to What Matters 关注重要的事情

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3290623

J. Teevan

引用次数: 0

Recurrent Recommendation with Local Coherence 具有局部一致性的经常性建议

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291024

Jianling Wang, James Caverlee

引用次数: 17

User Behavior Modeling for Web Image Search 网络图像搜索的用户行为建模

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291597

Xiaohui Xie

引用次数: 5

Taxonomy-Aware Multi-Hop Reasoning Networks for Sequential Recommendation 序列推荐的分类感知多跳推理网络

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3290972

Jin Huang, Z. Ren, Wayne Xin Zhao, Gaole He, Ji-Rong Wen, Daxiang Dong

{"title":"Taxonomy-Aware Multi-Hop Reasoning Networks for Sequential Recommendation","authors":"Jin Huang, Z. Ren, Wayne Xin Zhao, Gaole He, Ji-Rong Wen, Daxiang Dong","doi":"10.1145/3289600.3290972","DOIUrl":"https://doi.org/10.1145/3289600.3290972","url":null,"abstract":"In this paper, we focus on the task of sequential recommendation using taxonomy data. Existing sequential recommendation methods usually adopt a single vectorized representation for learning the overall sequential characteristics, and have a limited modeling capacity in capturing multi-grained sequential characteristics over context information. Besides, existing methods often directly take the feature vectors derived from context information as auxiliary input, which is difficult to fully exploit the structural patterns in context information for learning preference representations. To address above issues, we propose a novel Taxonomy-aware Multi-hop Reasoning Network, named TMRN, which integrates a basic GRU-based sequential recommender with an elaborately designed memory-based multi-hop reasoning architecture. For enhancing the reasoning capacity, we incorporate taxonomy data as structural knowledge to instruct the learning of our model. We associate the learning of user preference in sequential recommendation with the category hierarchy in the taxonomy. Given a user, for each recommendation, we learn a unique preference representation corresponding to each level in the taxonomy based on her/his overall sequential preference. In this way, the overall, coarse-grained preference representation can be gradually refined in different levels from general to specific, and we are able to capture the evolvement and refinement of user preference over the taxonomy, which makes our model highly explainable. Extensive experiments show that our proposed model is superior to state-of-the-art baselines in terms of both effectiveness and interpretability.","PeriodicalId":143253,"journal":{"name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124713266","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: 67

WSDM 2019 Tutorial on Health Search (HS2019): A Full-Day from Consumers to Clinicians WSDM 2019健康搜索教程(HS2019):从消费者到临床医生的一整天

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291379

B. Koopman, G. Zuccon

引用次数: 2

NAIRS: A Neural Attentive Interpretable Recommendation System NAIRS:一个神经关注可解释推荐系统

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3290609

Shuai Yu, Yongbo Wang, Min Yang, Baocheng Li, Qiang Qu, Jialie Shen

引用次数: 17

FAIRY: A Framework for Understanding Relationships Between Users' Actions and their Social Feeds FAIRY:一个理解用户行为和他们的社交动态之间关系的框架

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3290990

Azin Ghazimatin, Rishiraj Saha Roy, G. Weikum

{"title":"FAIRY: A Framework for Understanding Relationships Between Users' Actions and their Social Feeds","authors":"Azin Ghazimatin, Rishiraj Saha Roy, G. Weikum","doi":"10.1145/3289600.3290990","DOIUrl":"https://doi.org/10.1145/3289600.3290990","url":null,"abstract":"Users increasingly rely on social media feeds for consuming daily information. The items in a feed, such as news, questions, songs, etc., usually result from the complex interplay of a user's social contacts, her interests and her actions on the platform. The relationship of the user's own behavior and the received feed is often puzzling, and many users would like to have a clear explanation on why certain items were shown to them. Transparency and explainability are key concerns in the modern world of cognitive overload, filter bubbles, user tracking, and privacy risks. This paper presents FAIRY, a framework that systematically discovers, ranks, and explains relationships between users' actions and items in their social media feeds. We model the user's local neighborhood on the platform as an interaction graph, a form of heterogeneous information network constructed solely from information that is easily accessible to the concerned user. We posit that paths in this interaction graph connecting the user and her feed items can act as pertinent explanations for the user. These paths are scored with a learning-to-rank model that captures relevance and surprisal. User studies on two social platforms demonstrate the practical viability and user benefits of the FAIRY method.","PeriodicalId":143253,"journal":{"name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116415427","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: 10

Task Duration Estimation 任务时长估算

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3290997

Ryen W. White, Ahmed Hassan Awadallah

{"title":"Task Duration Estimation","authors":"Ryen W. White, Ahmed Hassan Awadallah","doi":"10.1145/3289600.3290997","DOIUrl":"https://doi.org/10.1145/3289600.3290997","url":null,"abstract":"Estimating how long a task will take to complete (i.e., the task duration) is important for many applications, including calendaring and project management. Population-scale calendar data contains distributional information about time allocated by individuals for tasks that may be useful to build computational models for task duration estimation. This study analyzes anonymized large-scale calendar appointment data from hundreds of thousands of individuals and millions of tasks to understand expected task durations and the longitudinal evolution in these durations. Machine-learned models are trained using the appointment data to estimate task duration. Study findings show that task attributes, including content (anonymized appointment subjects), context, and history, are correlated with time allocated for tasks. We also show that machine-learned models can be trained to estimate task duration, with multiclass classification accuracies of almost 80%. The findings have implications for understanding time estimation in populations, and in the design of support in digital assistants and calendaring applications to find time for tasks and to help people, especially those who are new to a task, block sufficient time for task completion.","PeriodicalId":143253,"journal":{"name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125564312","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: 21