Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献

Knowledge Tracing with Sequential Key-Value Memory Networks 基于顺序键值记忆网络的知识跟踪

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331195

Ghodai M. Abdelrahman, Qing Wang

{"title":"Knowledge Tracing with Sequential Key-Value Memory Networks","authors":"Ghodai M. Abdelrahman, Qing Wang","doi":"10.1145/3331184.3331195","DOIUrl":"https://doi.org/10.1145/3331184.3331195","url":null,"abstract":"Can machines trace human knowledge like humans? Knowledge tracing (KT) is a fundamental task in a wide range of applications in education, such as massive open online courses (MOOCs), intelligent tutoring systems, educational games, and learning management systems. It models dynamics in a student's knowledge states in relation to different learning concepts through their interactions with learning activities. Recently, several attempts have been made to use deep learning models for tackling the KT problem. Although these deep learning models have shown promising results, they have limitations: either lack the ability to go deeper to trace how specific concepts in a knowledge state are mastered by a student, or fail to capture long-term dependencies in an exercise sequence. In this paper, we address these limitations by proposing a novel deep learning model for knowledge tracing, namely Sequential Key-Value Memory Networks (SKVMN). This model unifies the strengths of recurrent modelling capacity and memory capacity of the existing deep learning KT models for modelling student learning. We have extensively evaluated our proposed model on five benchmark datasets. The experimental results show that (1) SKVMN outperforms the state-of-the-art KT models on all datasets, (2) SKVMN can better discover the correlation between latent concepts and questions, and (3) SKVMN can trace the knowledge state of students dynamics, and a leverage sequential dependencies in an exercise sequence for improved predication accuracy.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75499182","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: 96

Session details: Session 2A: Question Answering 会议详情:2A:问答环节

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3349678

C. Shah

引用次数: 0

Graph Intention Network for Click-through Rate Prediction in Sponsored Search 赞助搜索中点击率预测的图形意图网络

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331283

Feng Li, Zhenrui Chen, Pengjie Wang, Yi Ren, Di Zhang, Xiaoyu Zhu

{"title":"Graph Intention Network for Click-through Rate Prediction in Sponsored Search","authors":"Feng Li, Zhenrui Chen, Pengjie Wang, Yi Ren, Di Zhang, Xiaoyu Zhu","doi":"10.1145/3331184.3331283","DOIUrl":"https://doi.org/10.1145/3331184.3331283","url":null,"abstract":"Estimating click-through rate (CTR) accurately has an essential impact on improving user experience and revenue in sponsored search. For CTR prediction model, it is necessary to make out user's real-time search intention. Most of the current work is to mine their intentions based on users' real-time behaviors. However, it is difficult to capture the intention when user behaviors are sparse, causing thebehavior sparsity problem. Moreover, it is difficult for user to jump out of their specific historical behaviors for possible interest exploration, namelyweak generalization problem. We propose a new approach Graph Intention Network (GIN) based on co-occurrence commodity graph to mine user intention. By adopting multi-layered graph diffusion, GIN enriches user behaviors to solve the behavior sparsity problem. By introducing co-occurrence relationship of commodities to explore the potential preferences, the weak generalization problem is also alleviated. To the best of our knowledge, the GIN method is the first to introduce graph learning for user intention mining in CTR prediction and propose end-to-end joint training of graph learning and CTR prediction tasks in sponsored search. At present, GIN has achieved excellent offline results on the real-world data of the e-commerce platform outperforming existing deep learning models, and has been running stable tests online and achieved significant CTR improvements.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"146 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77660718","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: 42

SIGIR 2019 Tutorial on Explainable Recommendation and Search SIGIR 2019可解释推荐和搜索教程

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331390

Yongfeng Zhang, Jiaxin Mao, Qingyao Ai

引用次数: 8

Item Recommendation by Combining Relative and Absolute Feedback Data 结合相对和绝对反馈数据进行项目推荐

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331295

Saikishore Kalloori, Tianyu Li, F. Ricci

{"title":"Item Recommendation by Combining Relative and Absolute Feedback Data","authors":"Saikishore Kalloori, Tianyu Li, F. Ricci","doi":"10.1145/3331184.3331295","DOIUrl":"https://doi.org/10.1145/3331184.3331295","url":null,"abstract":"User preferences in the form of absolute feedback, s.a., ratings, are widely exploited in Recommender Systems (RSs). Recent research has explored the usage of preferences expressed with pairwise comparisons, which signal relative feedback. It has been shown that pairwise comparisons can be effectively combined with ratings, but, it is important to fine tune the technique that leverages both types of feedback. Previous approaches train a single model by converting ratings into pairwise comparisons, and then use only that type of data. However, we claim that these two types of preferences reveal different information about users interests and should be exploited differently. Hence, in this work, we develop a ranking technique that separately exploits absolute and relative preferences in a hybrid model. In particular, we propose a joint loss function which is computed on both absolute and relative preferences of users. Our proposed ranking model uses pairwise comparisons data to predict the user's preference order between pairs of items and uses ratings to push high rated (relevant) items to the top of the ranking. Experimental results on three different data sets demonstrate that the proposed technique outperforms competitive baseline algorithms on popular ranking-oriented evaluation metrics.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"521 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79020049","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: 6

AgentBuddy: an IR System based on Bandit Algorithms to Reduce Cognitive Load for Customer Care Agents AgentBuddy:一个基于Bandit算法的IR系统，以减少客户服务座席的认知负荷

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331408

Hrishikesh Ganu, Mithun Ghosh, Freddy Jose, S. Roshan

引用次数: 0

Towards Context-Aware Evaluation for Image Search 面向图像搜索的上下文感知评价

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331343

Yunqiu Shao, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma

引用次数: 1

A New Perspective on Score Standardization 分数标准化的新视角

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331315

Julián Urbano, Harlley Lima, A. Hanjalic

引用次数: 10

Quantifying Bias and Variance of System Rankings 系统排名的量化偏差和方差

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331356

G. Cormack, Maura R. Grossman

引用次数: 5

Unbiased Low-Variance Estimators for Precision and Related Information Retrieval Effectiveness Measures 精度和相关信息检索有效性度量的无偏低方差估计

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331355

G. Cormack, Maura R. Grossman

引用次数: 2