Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining最新文献_第9页

Session details: Session 11: Domain Transfer and Representation Learning 会议详情:会议11:领域转移和表示学习

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3310351

Laura Dietz

引用次数: 0

Session details: Session 10: Personalization and Characterizing User Behavior 第10部分:个性化和用户行为特征

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3310350

Milad Shokouhi

引用次数: 0

Social Attentional Memory Network: Modeling Aspect- and Friend-Level Differences in Recommendation 社会注意记忆网络:在推荐中建模方面和朋友水平的差异

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3290982

Chong Chen, Min Zhang, Yiqun Liu, Shaoping Ma

{"title":"Social Attentional Memory Network: Modeling Aspect- and Friend-Level Differences in Recommendation","authors":"Chong Chen, Min Zhang, Yiqun Liu, Shaoping Ma","doi":"10.1145/3289600.3290982","DOIUrl":"https://doi.org/10.1145/3289600.3290982","url":null,"abstract":"Social connections are known to be helpful for modeling users' potential preferences and improving the performance of recommender systems. However, in social-aware recommendations, there are two issues which influence the inference of users' preferences, and haven't been well-studied in most existing methods: First, the preferences of a user may only partially match that of his friends in certain aspects, especially when considering a user with diverse interests. Second, for an individual, the influence strength of his friends might be different, as not all friends are equally helpful for modeling his preferences in the system. To address the above issues, in this paper, we propose a novel Social Attentional Memory Network (SAMN) for social-aware recommendation. Specifically, we first design an attention-based memory module to learn user-friend relation vectors, which can capture the varying aspect attentions that a user share with his different friends. Then we build a friend-level attention component to adaptively select informative friends for user modeling. The two components are fused together to mutually enhance each other and lead to a finer extended model. Experimental results on three publicly available datasets show that the proposed SAMN model consistently and significantly outperforms the state-of-the-art recommendation methods. Furthermore, qualitative studies have been made to explore what the proposed attention-based memory module and friend-level attention have learnt, which provide insights into the model's learning process.","PeriodicalId":143253,"journal":{"name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","volume":"39 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":"133124877","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: 94

Comparative Summarisation of Rich Media Collections 富媒体集合的比较总结

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291603

Umanga Bista

{"title":"Comparative Summarisation of Rich Media Collections","authors":"Umanga Bista","doi":"10.1145/3289600.3291603","DOIUrl":"https://doi.org/10.1145/3289600.3291603","url":null,"abstract":"The goal of this thesis is to develop techniques for comparative summarisation of multimodal document collections. Comparative summarisation is extractive summarisation in comparative settings, where documents form two or more groups, e.g. articles on the same topic but from different sources. Comparative summarisation involves, not only, selecting representative and diverse samples within groups, but also samples that highlight commonalities and differences between the groups. We posit that comparative summarisation is a fruitful problem for diverse use cases, such as comparing content over time, authors, or distinct view points. We formulate the problem of comparative summarisation by reducing it to binary classification problem and define objectives to incorporate representativeness, diversity and comparativeness. We design new automatic and crowd-sourced evaluation protocols for summarisation evaluation that scales much better than the evaluations requiring manually created ground truth summaries. We show the efficacy of the approach in a newly curated datasets of controversial news topics. We plan to develop new collection comparison methods for multimodal document collections.","PeriodicalId":143253,"journal":{"name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","volume":"36 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":"133206479","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: 1

WassRank: Listwise Document Ranking Using Optimal Transport Theory WassRank:使用最优传输理论的列表文档排序

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291006

Haitao Yu, A. Jatowt, Hideo Joho, J. Jose, X. Yang, Long Chen

{"title":"WassRank: Listwise Document Ranking Using Optimal Transport Theory","authors":"Haitao Yu, A. Jatowt, Hideo Joho, J. Jose, X. Yang, Long Chen","doi":"10.1145/3289600.3291006","DOIUrl":"https://doi.org/10.1145/3289600.3291006","url":null,"abstract":"Learning to rank has been intensively studied and has shown great value in many fields, such as web search, question answering and recommender systems. This paper focuses on listwise document ranking, where all documents associated with the same query in the training data are used as the input. We propose a novel ranking method, referred to as WassRank, under which the problem of listwise document ranking boils down to the task of learning the optimal ranking function that achieves the minimum Wasserstein distance. Specifically, given the query level predictions and the ground truth labels, we first map them into two probability vectors. Analogous to the optimal transport problem, we view each probability vector as a pile of relevance mass with peaks indicating higher relevance. The listwise ranking loss is formulated as the minimum cost (the Wasserstein distance) of transporting (or reshaping) the pile of predicted relevance mass so that it matches the pile of ground-truth relevance mass. The smaller the Wasserstein distance is, the closer the prediction gets to the ground-truth. To better capture the inherent relevance-based order information among documents with different relevance labels and lower the variance of predictions for documents with the same relevance label, ranking-specific cost matrix is imposed. To validate the effectiveness of WassRank, we conduct a series of experiments on two benchmark collections. The experimental results demonstrate that: compared with four non-trivial listwise ranking methods (i.e., LambdaRank, ListNet, ListMLE and ApxNDCG), WassRank can achieve substantially improved performance in terms of nDCG and ERR across different rank positions. Specifically, the maximum improvements of WassRank over LambdaRank, ListNet, ListMLE and ApxNDCG in terms of nDCG@1 are 15%, 5%, 7%, 5%, respectively.","PeriodicalId":143253,"journal":{"name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","volume":"32 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":"132818189","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: 17

How A/B Tests Could Go Wrong: Automatic Diagnosis of Invalid Online Experiments A/B测试如何出错:无效在线实验的自动诊断

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291000

Nanyu Chen, Min Liu, Ya Xu

引用次数: 45

Alexa Everywhere: AI for Daily Convenience Alexa无处不在:日常便利的人工智能

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291377

R. Prasad

引用次数: 1

Uncovering Hidden Structure in Sequence Data via Threading Recurrent Models 利用线程循环模型揭示序列数据中的隐藏结构

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291036

M. Zaheer, Amr Ahmed, Y. Wang, Daniel Silva, Marc Najork, Yuchen Wu, Shibani Sanan, Surojit Chatterjee

{"title":"Uncovering Hidden Structure in Sequence Data via Threading Recurrent Models","authors":"M. Zaheer, Amr Ahmed, Y. Wang, Daniel Silva, Marc Najork, Yuchen Wu, Shibani Sanan, Surojit Chatterjee","doi":"10.1145/3289600.3291036","DOIUrl":"https://doi.org/10.1145/3289600.3291036","url":null,"abstract":"Long Short-Term Memory (LSTM) is one of the most powerful sequence models for user browsing history citetan2016improved,korpusik2016recurrent or natural language text citemikolov2010recurrent.Despite the strong performance, it has not gained popularity for user-facing applications, mainly owing to a large number of parameters and lack of interpretability. Recently citetzaheer2017latent introduced latent LSTM Allocation (LLA) to address these problems by incorporating topic models with LSTM, where the topic model maps observed words in each sequence to topics that evolve using an LSTM model. In our experiments, we found the resulting model, although powerful and interpretable, to show shortcomings when applied to sequence data that exhibit multi-modes of behaviors with abrupt dynamic changes. To address this problem we introduce thLLA: a threading LLA model. thLLA has the ability to break each sequence into a set of segments and then model the dynamic in each segment using an LSTM mixture. In that way, thLLA can model abrupt changes in sequence dynamics and provides a better fit for sequence data while still being interpretable and requiring fewer parameters. In addition, thLLA uncovers hidden themes in the data via its dynamic mixture components. However, such generalization and interpretability come at a cost of complex dependence structure, for which inference would be extremely non-trivial. To remedy this, we present an efficient sampler based on particle MCMC method for inference that can draw from the joint posterior directly. Experimental results confirm the superiority of thLLA and the stability of the new inference algorithm on a variety of domains.","PeriodicalId":143253,"journal":{"name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","volume":"4 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":"124816853","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: 2

Overlapping Community Detection in Static and Dynamic Social Networks 静态和动态社会网络中的重叠社区检测

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3291602

Renny Márquez, R. Weber

引用次数: 10

clstk: The Cross-Lingual Summarization Tool-Kit clstk:跨语言摘要工具包

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining Pub Date : 2019-01-30 DOI: 10.1145/3289600.3290614

Nisarg Jhaveri, Manish Gupta, Vasudeva Varma

引用次数: 6