Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献_第6页

Proactive Suggestion Generation: Data and Methods for Stepwise Task Assistance 主动建议生成:逐步任务协助的数据和方法

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401272

E. Nouri, Robert Sim, Adam Fourney, Ryen W. White

引用次数: 3

DVGAN

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401084

Jiongnan Liu, Zhicheng Dou, Xiaojie Wang, Shuqi Lu, Ji-rong Wen

{"title":"DVGAN","authors":"Jiongnan Liu, Zhicheng Dou, Xiaojie Wang, Shuqi Lu, Ji-rong Wen","doi":"10.1145/3397271.3401084","DOIUrl":"https://doi.org/10.1145/3397271.3401084","url":null,"abstract":"Search result diversification aims to retrieve diverse results to cover as many subtopics related to the query as possible. Recent studies showed that supervised diversification models are able to outperform the heuristic approaches, by automatically learning a diversification function other than using manually designed score functions. The main challenge of training a diversification model is the lack of high-quality training samples. Due to the involvement of dependence between documents in the ranker, it is very hard for training algorithms to select effective positive and negative ranking lists to train a reliable ranking model, given a large number of candidate documents within which different documents are relevant to different subtopics. To tackle this problem, we propose a supervised diversification framework based on Generative Adversarial Network (GAN). It consists of a generator and a discriminator interacting with each other in a minimax game. Specifically, the generator generates more confusing negative samples for the discriminator, and the discriminator sends back complementary ranking signals to the generator. Furthermore, we explicitly exploit subtopics in the generator, whereas focusing on modeling document similarity in the discriminator. Through such a minimax game, we are able to obtain better ranking models by combining ranking signals learned by the generator and the discriminator. Experimental results on the TREC Web Track dataset show that the proposed method can significantly outperform existing diversification methods.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114185903","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: 20

HME HME

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401049

Shanshan Feng, Lucas Vinh Tran, G. Cong, Lisi Chen, Jing Li, Fan Li

{"title":"HME","authors":"Shanshan Feng, Lucas Vinh Tran, G. Cong, Lisi Chen, Jing Li, Fan Li","doi":"10.1145/3397271.3401049","DOIUrl":"https://doi.org/10.1145/3397271.3401049","url":null,"abstract":"With the increasing popularity of location-aware social media services, next-Point-of-Interest (POI) recommendation has gained significant research interest. The key challenge of next-POI recommendation is to precisely learn users' sequential movements from sparse check-in data. To this end, various embedding methods have been proposed to learn the representations of check-in data in the Euclidean space. However, their ability to learn complex patterns, especially hierarchical structures, is limited by the dimensionality of the Euclidean space. To this end, we propose a new research direction that aims to learn the representations of check-in activities in a hyperbolic space, which yields two advantages. First, it can effectively capture the underlying hierarchical structures, which are implied by the power-law distributions of user movements. Second, it provides high representative strength and enables the check-in data to be effectively represented in a low-dimensional space. Specifically, to solve the next-POI recommendation task, we propose a novel hyperbolic metric embedding (HME) model, which projects the check-in data into a hyperbolic space. The HME jointly captures sequential transition, user preference, category and region information in a unified approach by learning embeddings in a shared hyperbolic space. To the best of our knowledge, this is the first study to explore a non-Euclidean embedding model for next-POI recommendation. We conduct extensive experiments on three check-in datasets to demonstrate the superiority of our hyperbolic embedding approach over the state-of-the-art next-POI recommendation algorithms. Moreover, we conduct experiments on another four online transaction datasets for next-item recommendation to further demonstrate the generality of our proposed model.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125553025","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: 74

Query by Example for Cross-Lingual Event Retrieval 跨语言事件检索的示例查询

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401283

Sheikh Muhammad Sarwar, J. Allan

引用次数: 7

Personalized Query Suggestions 个性化查询建议

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401331

Jianling Zhong, Weiwei Guo, Huiji Gao, Bo Long

{"title":"Personalized Query Suggestions","authors":"Jianling Zhong, Weiwei Guo, Huiji Gao, Bo Long","doi":"10.1145/3397271.3401331","DOIUrl":"https://doi.org/10.1145/3397271.3401331","url":null,"abstract":"With the exponential growth of information on the internet, users have been relying on search engines for finding the precise documents. However, user queries are often short. The inherent ambiguity of short queries imposes great challenges for search engines to understand user intent. Query suggestion is one key technique for search engines to augment user queries so that they can better understand user intent. In the past, query suggestions have been relying on either term-frequency--based methods with little semantic understanding of the query, or word-embedding--based methods with little personalization efforts. Here, we present a sequence-to-sequence-model--based query suggestion framework that is capable of modeling structured, personalized features and unstructured query texts naturally. This capability opens up the opportunity to better understand query semantics and user intent at the same time. As the largest professional network, LinkedIn has the advantage of utilizing a rich amount of accurate member profile information to personalize query suggestions. We applied this framework in the LinkedIn production traffic and showed that personalized query suggestions significantly improved member search experience as measured by key business metrics at LinkedIn.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124937221","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: 12

Finding the Best of Both Worlds: Faster and More Robust Top-k Document Retrieval 两全其美:更快更健壮的Top-k文档检索

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401076

O. Khattab, Mohammad Hammoud, T. Elsayed

引用次数: 12

Multi-Document Answer Generation for Non-Factoid Questions 非虚构问题的多文档答案生成

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401449

Valeriia Bolotova-Baranova

{"title":"Multi-Document Answer Generation for Non-Factoid Questions","authors":"Valeriia Bolotova-Baranova","doi":"10.1145/3397271.3401449","DOIUrl":"https://doi.org/10.1145/3397271.3401449","url":null,"abstract":"The current research will be devoted to the challenging and under-investigated task of multi-source answer generation for complex non-factoid questions. We will start with experimenting with generative models on one particular type of non-factoid questions - instrumental/procedural questions which often start with \"how-to\". For this, a new dataset, comprised of more than 100,000 QA-pairs which were crawled from a dedicated web-resource where each answer has a set of references to the articles it was written upon, will be used. We will also compare different ways of model evaluation to choose a metric which better correlates with human assessment. To be able to do this, the way people evaluate answers to non-factoid questions and set some formal criteria of what makes a good quality answer is needed to be understood. Eye-tracking and crowdsourcing methods will be employed to study how users interact with answers and evaluate them, and how the answer features correlate with task complexity. We hope that our research will help to redefine the way users interact and work with search engines so as to transform IR finally into the answer retrieval systems that users have always desired.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131735555","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

Multi-source Domain Adaptation for Sentiment Classification with Granger Causal Inference 基于格兰杰因果推理的多源领域自适应情感分类

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401314

Min Yang, Ying Shen, Xiaojun Chen, Chengming Li

引用次数: 5

AIIS: The SIGIR 2020 Workshop on Applied Interactive Information Systems 2020年SIGIR应用交互信息系统研讨会

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401461

Hongshen Chen, Z. Ren, Pengjie Ren, Dawei Yin, Xiaodong He

{"title":"AIIS: The SIGIR 2020 Workshop on Applied Interactive Information Systems","authors":"Hongshen Chen, Z. Ren, Pengjie Ren, Dawei Yin, Xiaodong He","doi":"10.1145/3397271.3401461","DOIUrl":"https://doi.org/10.1145/3397271.3401461","url":null,"abstract":"Nowadays, intelligent information systems, especially the interactive information systems (e.g., conversational interaction systems like Siri, and Cortana; news feed recommender systems, and interactive search engines, etc.), are ubiquitous in real-world applications. These systems either converse with users explicitly through natural languages, or mine users interests and respond to users requests implicitly. Interactivity has become a crucial element towards intelligent information systems. Despite the fact that interactive information systems have gained significant progress, there are still many challenges to be addressed when applying these models to real-world scenarios. This half day workshop explores challenges and potential research, development, and application directions in applied interactive information systems. We aim to discuss the issues of applying interactive information models to production systems, as well as to shed some light on the fundamental characteristics, i.e., interactivity and applicability, of different interactive tasks. We welcome practical, theoretical, experimental, and methodological studies that advances the interactivity towards intelligent information systems. The workshop aims to bring together a diverse set of practitioners and researchers interested in investigating the interaction between human and information systems to develop more intelligent information systems.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133535892","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

How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models 数据集特征如何影响协同推荐模型的鲁棒性

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401046

Yashar Deldjoo, T. D. Noia, E. Sciascio, Felice Antonio Merra

{"title":"How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models","authors":"Yashar Deldjoo, T. D. Noia, E. Sciascio, Felice Antonio Merra","doi":"10.1145/3397271.3401046","DOIUrl":"https://doi.org/10.1145/3397271.3401046","url":null,"abstract":"Shilling attacks against collaborative filtering (CF) models are characterized by several fake user profiles mounted on the system by an adversarial party to harvest recommendation outcomes toward a malicious desire. The vulnerability of CF models is directly tied with their reliance on the underlying interaction data ---like user-item rating matrix (URM) --- to train their models and their inherent inability to distinguish genuine profiles from non-genuine ones. The majority of works conducted so far for analyzing shilling attacks mainly focused on properties such as confronted recommendation models, recommendation outputs, and even users under attack. The under-researched element has been the impact of data characteristics on the effectiveness of shilling attacks on CF models. Toward this goal, this work presents a systematic and in-depth study by using an analytical modeling approach built on a regression model to test the hypothesis of whether URM properties can impact the outcome of CF recommenders under a shilling attack. We ran extensive experiments involving 97200 simulations on three different domains (movie, business, and music), and showed that URM properties considerably affect the robustness of CF models in shilling attack scenarios. Obtained results can be of great help for the system designer in understanding the cause of variations in a recommender system performance due to a shilling attack.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133738276","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: 41